Injection of user feedback into language model adaptation

ABSTRACT

The present disclosure generally relates to updating a language model based on user feedback. Based on a user text input, a language model predicts a set of tokens and an action that will be taken by the user in response to the predicted set of tokens. If the predicted action does not match a detected actual user action, the language model is updated to reflect the user feedback by modifying an output token probability distribution based on the actual user action and updating the language model to converge with a target language model using the modified output token probability distribution.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application No.63/348,362, entitled “INJECTION OF USER FEEDBACK INTO LANGUAGE MODELADAPTATION,” and filed Jun. 2, 2022, the content of which is herebyincorporated by reference.

FIELD

The present disclosure relates generally to updating a language modelbased on user data.

BACKGROUND

Language models can be used to generate text predictions based on textinput by a user, such as corrections to the input text and/orpredictions of the next text following the text input. The languagemodels can be trained on training data including a very large amount oftext samples, including user-specific text samples. Users may providefeedback in response to the predicted text, including accepting theprediction or rejecting the prediction, for instance, by explicitlydismissing the prediction, accepting an unexpected prediction, ormanually inputting unexpected text.

BRIEF SUMMARY

Some techniques for training and updating language models usingelectronic devices, however, are generally cumbersome and inefficient.For example, although both user feedback and user-specific text samplesprovide useful data reflecting a user's preferences, compared to thesize of a general training corpus, an average user produces very littletext and even fewer responses to predictions. This relative scarcity ofuser data makes updating the language model in a way that reflects theuser data difficult. Additionally, user feedback can indicate both auser action and a user-input token sequence. However, due to thedifference in the size and nature between action predictions and tokenpredictions, integrating both types of user feedback information canincrease latency.

Accordingly, the present technique provides electronic devices withfaster, more efficient methods and interfaces for updating a languagemodel based on user data. Such methods and interfaces optionallycomplement or replace other methods for updating a language model basedon user feedback. Adjusting a language model based on user feedbackprior to adapting (e.g., comparing) the language model to a target usingan adversarial framework efficiently integrates the user feedback intoan update process. Such methods and interfaces additionally reduce thecognitive burden on a user and produce a more efficient human-machineinterface by providing more accurate and useful text prediction for thespecific user. For battery-operated computing devices, such methods andinterfaces conserve power and increase the time between battery charges.

Example processes are disclosed herein. An example process for updatinga language model includes, at an electronic device with one or moreprocessors and a memory, receiving one or more input tokens; in responseto receiving the one or more input tokens, predicting, using a firstoutput token probability distribution drawn from a first overallprobability distribution of a first language model, a first set of oneor more output tokens; generating a predicted user action to beperformed on the first set of one or more output tokens; providing anoutput including the first set of one or more output tokens; detecting afirst user action responding to the first set of one or more outputtokens; and, in accordance with a determination that the first useraction does not match the predicted user action: generating a modifiedoutput token probability distribution based on the first user action;and, based on the modified output token probability distribution,updating the first overall token probability distribution to converge toa second overall token probability distribution.

Example electronic devices are disclosed herein. An example electronicdevice includes one or more processors; a memory; and one or moreprograms, wherein the one or more programs are stored in the memory andconfigured to be executed by the one or more processors, the one or moreprograms including instructions for receiving one or more input tokens;in response to receiving the one or more input tokens, predicting, usinga first output token probability distribution drawn from a first overallprobability distribution of a first language model, a first set of oneor more output tokens; generating a predicted user action to beperformed on the first set of one or more output tokens; providing anoutput including the first set of one or more output tokens; detecting afirst user action responding to the first set of one or more outputtokens; and, in accordance with a determination that the first useraction does not match the predicted user action: generating a modifiedoutput token probability distribution based on the first user action;and, based on the modified output token probability distribution,updating the first overall token probability distribution to converge toa second overall token probability distribution.

Example non-transitory computer-readable storage media are disclosedherein. An example non-transitory computer-readable storage mediumstoring one or more programs, the one or more programs comprisinginstructions, which when executed by one or more processors of a firstelectronic device, cause the first electronic device to receive one ormore input tokens; in response to receiving the one or more inputtokens, predict, using a first output token probability distributiondrawn from a first overall probability distribution of a first languagemodel, a first set of one or more output tokens; generate a predicteduser action to be performed on the first set of one or more outputtokens; provide an output including the first set of one or more outputtokens; detect a first user action responding to the first set of one ormore output tokens; and, in accordance with a determination that thefirst user action does not match the predicted user action: generate amodified output token probability distribution based on the first useraction; and, based on the modified output token probabilitydistribution, update the first overall token probability distribution toconverge to a second overall token probability distribution.

Example transitory computer-readable storage media are disclosed herein.An example transitory computer readable storage medium storing one ormore programs, the one or more programs comprising instructions, whichwhen executed by one or more processors of a first electronic device,cause the first electronic device to receive one or more input tokens;in response to receiving the one or more input tokens, predict, using afirst output token probability distribution drawn from a first overallprobability distribution of a first language model, a first set of oneor more output tokens; generate a predicted user action to be performedon the first set of one or more output tokens; provide an outputincluding the first set of one or more output tokens; detect a firstuser action responding to the first set of one or more output tokens;and, in accordance with a determination that the first user action doesnot match the predicted user action: generate a modified output tokenprobability distribution based on the first user action; and, based onthe modified output token probability distribution, update the firstoverall token probability distribution to converge to a second overalltoken probability distribution.

Executable instructions for performing these functions are, optionally,included in a non-transitory computer-readable storage medium or othercomputer program product configured for execution by one or moreprocessors. Executable instructions for performing these functions are,optionally, included in a transitory computer-readable storage medium orother computer program product configured for execution by one or moreprocessors.

Thus, devices are provided with faster, more efficient methods andinterfaces for updating a language model based on user data, therebyincreasing the effectiveness, efficiency, and user satisfaction withsuch devices. Such methods and interfaces may complement or replaceother methods for updating a language model based on user data.

DESCRIPTION OF THE FIGURES

For a better understanding of the various described embodiments,reference should be made to the Description of Embodiments below, inconjunction with the following drawings in which like reference numeralsrefer to corresponding parts throughout the figures.

FIG. 1A is a block diagram illustrating a portable multifunction devicewith a touch-sensitive display in accordance with some embodiments.

FIG. 1B is a block diagram illustrating exemplary components for eventhandling in accordance with some embodiments.

FIG. 2 illustrates a portable multifunction device having a touch screenin accordance with some embodiments.

FIG. 3 is a block diagram of an exemplary multifunction device with adisplay and a touch-sensitive surface in accordance with someembodiments.

FIG. 4A illustrates an exemplary user interface for a menu ofapplications on a portable multifunction device in accordance with someembodiments.

FIG. 4B illustrates an exemplary user interface for a multifunctiondevice with a touch-sensitive surface that is separate from the displayin accordance with some embodiments.

FIG. 5A illustrates a personal electronic device in accordance with someembodiments.

FIG. 5B is a block diagram illustrating a personal electronic device inaccordance with some embodiments.

FIG. 6 illustrates a system for updating a language model based on userfeedback in accordance with some embodiments.

FIGS. 7A-7D illustrate text prediction at an electronic device inaccordance with some embodiments.

FIGS. 8A-8B illustrate a flow diagram for a process of updating alanguage model based on user feedback in accordance with someembodiments.

DESCRIPTION OF EMBODIMENTS

The following description sets forth exemplary methods, parameters, andthe like. It should be recognized, however, that such description is notintended as a limitation on the scope of the present disclosure but isinstead provided as a description of exemplary embodiments.

There is a need for electronic devices that provide efficient methodsand interfaces for updating a language model based on user feedback. Forexample, adjusting a language model based on user feedback prior toadapting (e.g., comparing) the language model to a target using anadversarial framework efficiently integrates the user feedback into anupdate process. Such techniques can reduce the cognitive burden on auser by improving the accuracy and usefulness of text prediction,thereby enhancing productivity. Further, such techniques can reduceprocessor and battery power otherwise wasted on inefficient languagemodel updates and/or redundant or unnecessary user inputs.

Below, FIGS. 1A-1B, 2, 3, 4A-4B, and 5A-5B provide a description ofexemplary devices for performing the techniques for managing eventnotifications. FIG. 6 illustrates an exemplary system for updating alanguage model based on user data. FIGS. 7A-7D illustrate textprediction at an electronic device, according to some embodiments. FIGS.8A-8B illustrate an exemplary flow diagram for updating a language modelbased on user data. The systems and user interfaces in FIGS. 6-7D areused to illustrate the processes described below, including theprocesses in FIGS. 8A-8B.

The processes described below enhance the operability of the devices andmake the user-device interfaces more efficient (e.g., by helping theuser to provide proper inputs and reducing user mistakes whenoperating/interacting with the device) through various techniques,including by providing improved visual feedback to the user, reducingthe number of inputs needed to perform an operation, providingadditional control options without cluttering the user interface withadditional displayed controls, performing an operation when a set ofconditions has been met without requiring further user input,incorporating user feedback in an accurate and timely manner, and/oradditional techniques. These techniques also reduce power usage andimprove battery life of the device by enabling the user to use thedevice more quickly and efficiently.

In addition, in methods described herein where one or more steps arecontingent upon one or more conditions having been met, it should beunderstood that the described method can be repeated in multiplerepetitions so that over the course of the repetitions all of theconditions upon which steps in the method are contingent have been metin different repetitions of the method. For example, if a methodrequires performing a first step if a condition is satisfied, and asecond step if the condition is not satisfied, then a person of ordinaryskill would appreciate that the claimed steps are repeated until thecondition has been both satisfied and not satisfied, in no particularorder. Thus, a method described with one or more steps that arecontingent upon one or more conditions having been met could berewritten as a method that is repeated until each of the conditionsdescribed in the method has been met. This, however, is not required ofsystem or computer readable medium claims where the system or computerreadable medium contains instructions for performing the contingentoperations based on the satisfaction of the corresponding one or moreconditions and thus is capable of determining whether the contingencyhas or has not been satisfied without explicitly repeating steps of amethod until all of the conditions upon which steps in the method arecontingent have been met. A person having ordinary skill in the artwould also understand that, similar to a method with contingent steps, asystem or computer readable storage medium can repeat the steps of amethod as many times as are needed to ensure that all of the contingentsteps have been performed.

Although the following description uses terms “first,” “second,” etc. todescribe various elements, these elements should not be limited by theterms. In some embodiments, these terms are used to distinguish oneelement from another. For example, a first touch could be termed asecond touch, and, similarly, a second touch could be termed a firsttouch, without departing from the scope of the various describedembodiments. In some embodiments, the first touch and the second touchare two separate references to the same touch. In some embodiments, thefirst touch and the second touch are both touches, but they are not thesame touch.

The terminology used in the description of the various describedembodiments herein is for the purpose of describing particularembodiments only and is not intended to be limiting. As used in thedescription of the various described embodiments and the appendedclaims, the singular forms “a,” “an,” and “the” are intended to includethe plural forms as well, unless the context clearly indicatesotherwise. It will also be understood that the term “and/or” as usedherein refers to and encompasses any and all possible combinations ofone or more of the associated listed items. It will be furtherunderstood that the terms “includes,” “including,” “comprises,” and/or“comprising,” when used in this specification, specify the presence ofstated features, integers, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof.

The term “if” is, optionally, construed to mean “when” or “upon” or “inresponse to determining” or “in response to detecting,” depending on thecontext. Similarly, the phrase “if it is determined” or “if [a statedcondition or event] is detected” is, optionally, construed to mean “upondetermining” or “in response to determining” or “upon detecting [thestated condition or event]” or “in response to detecting [the statedcondition or event],” depending on the context.

Embodiments of electronic devices, user interfaces for such devices, andassociated processes for using such devices are described. In someembodiments, the device is a portable communications device, such as amobile telephone, that also contains other functions, such as PDA and/ormusic player functions. Exemplary embodiments of portable multifunctiondevices include, without limitation, the iPhone®, iPod Touch®, and iPad®devices from Apple Inc. of Cupertino, California. Other portableelectronic devices, such as laptops or tablet computers withtouch-sensitive surfaces (e.g., touch screen displays and/or touchpads),are, optionally, used. It should also be understood that, in someembodiments, the device is not a portable communications device, but isa desktop computer with a touch-sensitive surface (e.g., a touch screendisplay and/or a touchpad). In some embodiments, the electronic deviceis a computer system that is in communication (e.g., via wirelesscommunication, via wired communication) with a display generationcomponent. The display generation component is configured to providevisual output, such as display via a CRT display, display via an LEDdisplay, or display via image projection. In some embodiments, thedisplay generation component is integrated with the computer system. Insome embodiments, the display generation component is separate from thecomputer system. As used herein, “displaying” content includes causingto display the content (e.g., video data rendered or decoded by displaycontroller 156) by transmitting, via a wired or wireless connection,data (e.g., image data or video data) to an integrated or externaldisplay generation component to visually produce the content.

In the discussion that follows, an electronic device that includes adisplay and a touch-sensitive surface is described. It should beunderstood, however, that the electronic device optionally includes oneor more other physical user-interface devices, such as a physicalkeyboard, a mouse, and/or a joystick.

The device typically supports a variety of applications, such as one ormore of the following: a drawing application, a presentationapplication, a word processing application, a website creationapplication, a disk authoring application, a spreadsheet application, agaming application, a telephone application, a video conferencingapplication, an e-mail application, an instant messaging application, aworkout support application, a photo management application, a digitalcamera application, a digital video camera application, a web browsingapplication, a digital music player application, and/or a digital videoplayer application.

The various applications that are executed on the device optionally useat least one common physical user-interface device, such as thetouch-sensitive surface. One or more functions of the touch-sensitivesurface as well as corresponding information displayed on the deviceare, optionally, adjusted and/or varied from one application to the nextand/or within a respective application. In this way, a common physicalarchitecture (such as the touch-sensitive surface) of the deviceoptionally supports the variety of applications with user interfacesthat are intuitive and transparent to the user.

Attention is now directed toward embodiments of portable devices withtouch-sensitive displays. FIG. 1A is a block diagram illustratingportable multifunction device 100 with touch-sensitive display system112 in accordance with some embodiments. Touch-sensitive display 112 issometimes called a “touch screen” for convenience and is sometimes knownas or called a “touch-sensitive display system.” Device 100 includesmemory 102 (which optionally includes one or more computer-readablestorage mediums), memory controller 122, one or more processing units(CPUs) 120, peripherals interface 118, RF circuitry 108, audio circuitry110, speaker 111, microphone 113, input/output (I/O) subsystem 106,other input control devices 116, and external port 124. Device 100optionally includes one or more optical sensors 164. Device 100optionally includes one or more contact intensity sensors 165 fordetecting intensity of contacts on device 100 (e.g., a touch-sensitivesurface such as touch-sensitive display system 112 of device 100).Device 100 optionally includes one or more tactile output generators 167for generating tactile outputs on device 100 (e.g., generating tactileoutputs on a touch-sensitive surface such as touch-sensitive displaysystem 112 of device 100 or touchpad 355 of device 300). Thesecomponents optionally communicate over one or more communication busesor signal lines 103.

As used in the specification and claims, the term “intensity” of acontact on a touch-sensitive surface refers to the force or pressure(force per unit area) of a contact (e.g., a finger contact) on thetouch-sensitive surface, or to a substitute (proxy) for the force orpressure of a contact on the touch-sensitive surface. The intensity of acontact has a range of values that includes at least four distinctvalues and more typically includes hundreds of distinct values (e.g., atleast 256). Intensity of a contact is, optionally, determined (ormeasured) using various approaches and various sensors or combinationsof sensors. For example, one or more force sensors underneath oradjacent to the touch-sensitive surface are, optionally, used to measureforce at various points on the touch-sensitive surface. In someimplementations, force measurements from multiple force sensors arecombined (e.g., a weighted average) to determine an estimated force of acontact. Similarly, a pressure-sensitive tip of a stylus is, optionally,used to determine a pressure of the stylus on the touch-sensitivesurface. Alternatively, the size of the contact area detected on thetouch-sensitive surface and/or changes thereto, the capacitance of thetouch-sensitive surface proximate to the contact and/or changes thereto,and/or the resistance of the touch-sensitive surface proximate to thecontact and/or changes thereto are, optionally, used as a substitute forthe force or pressure of the contact on the touch-sensitive surface. Insome implementations, the substitute measurements for contact force orpressure are used directly to determine whether an intensity thresholdhas been exceeded (e.g., the intensity threshold is described in unitscorresponding to the substitute measurements). In some implementations,the substitute measurements for contact force or pressure are convertedto an estimated force or pressure, and the estimated force or pressureis used to determine whether an intensity threshold has been exceeded(e.g., the intensity threshold is a pressure threshold measured in unitsof pressure). Using the intensity of a contact as an attribute of a userinput allows for user access to additional device functionality that mayotherwise not be accessible by the user on a reduced-size device withlimited real estate for displaying affordances (e.g., on atouch-sensitive display) and/or receiving user input (e.g., via atouch-sensitive display, a touch-sensitive surface, or aphysical/mechanical control such as a knob or a button).

As used in the specification and claims, the term “tactile output”refers to physical displacement of a device relative to a previousposition of the device, physical displacement of a component (e.g., atouch-sensitive surface) of a device relative to another component(e.g., housing) of the device, or displacement of the component relativeto a center of mass of the device that will be detected by a user withthe user's sense of touch. For example, in situations where the deviceor the component of the device is in contact with a surface of a userthat is sensitive to touch (e.g., a finger, palm, or other part of auser's hand), the tactile output generated by the physical displacementwill be interpreted by the user as a tactile sensation corresponding toa perceived change in physical characteristics of the device or thecomponent of the device. For example, movement of a touch-sensitivesurface (e.g., a touch-sensitive display or trackpad) is, optionally,interpreted by the user as a “down click” or “up click” of a physicalactuator button. In some cases, a user will feel a tactile sensationsuch as an “down click” or “up click” even when there is no movement ofa physical actuator button associated with the touch-sensitive surfacethat is physically pressed (e.g., displaced) by the user's movements. Asanother example, movement of the touch-sensitive surface is, optionally,interpreted or sensed by the user as “roughness” of the touch-sensitivesurface, even when there is no change in smoothness of thetouch-sensitive surface. While such interpretations of touch by a userwill be subject to the individualized sensory perceptions of the user,there are many sensory perceptions of touch that are common to a largemajority of users. Thus, when a tactile output is described ascorresponding to a particular sensory perception of a user (e.g., an “upclick,” a “down click,” “roughness”), unless otherwise stated, thegenerated tactile output corresponds to physical displacement of thedevice or a component thereof that will generate the described sensoryperception for a typical (or average) user.

It should be appreciated that device 100 is only one example of aportable multifunction device, and that device 100 optionally has moreor fewer components than shown, optionally combines two or morecomponents, or optionally has a different configuration or arrangementof the components. The various components shown in FIG. 1A areimplemented in hardware, software, or a combination of both hardware andsoftware, including one or more signal processing and/orapplication-specific integrated circuits.

Memory 102 optionally includes high-speed random access memory andoptionally also includes non-volatile memory, such as one or moremagnetic disk storage devices, flash memory devices, or othernon-volatile solid-state memory devices. Memory controller 122optionally controls access to memory 102 by other components of device100.

Peripherals interface 118 can be used to couple input and outputperipherals of the device to CPU 120 and memory 102. The one or moreprocessors 120 run or execute various software programs (such ascomputer programs (e.g., including instructions)) and/or sets ofinstructions stored in memory 102 to perform various functions fordevice 100 and to process data. In some embodiments, peripheralsinterface 118, CPU 120, and memory controller 122 are, optionally,implemented on a single chip, such as chip 104. In some otherembodiments, they are, optionally, implemented on separate chips.

RF (radio frequency) circuitry 108 receives and sends RF signals, alsocalled electromagnetic signals. RF circuitry 108 converts electricalsignals to/from electromagnetic signals and communicates withcommunications networks and other communications devices via theelectromagnetic signals. RF circuitry 108 optionally includes well-knowncircuitry for performing these functions, including but not limited toan antenna system, an RF transceiver, one or more amplifiers, a tuner,one or more oscillators, a digital signal processor, a CODEC chipset, asubscriber identity module (SIM) card, memory, and so forth. RFcircuitry 108 optionally communicates with networks, such as theInternet, also referred to as the World Wide Web (WWW), an intranetand/or a wireless network, such as a cellular telephone network, awireless local area network (LAN) and/or a metropolitan area network(MAN), and other devices by wireless communication. The RF circuitry 108optionally includes well-known circuitry for detecting near fieldcommunication (NFC) fields, such as by a short-range communicationradio. The wireless communication optionally uses any of a plurality ofcommunications standards, protocols, and technologies, including but notlimited to Global System for Mobile Communications (GSM), Enhanced DataGSM Environment (EDGE), high-speed downlink packet access (HSDPA),high-speed uplink packet access (HSUPA), Evolution, Data-Only (EV-DO),HSPA, HSPA+, Dual-Cell HSPA (DC-HSPDA), long term evolution (LTE), nearfield communication (NFC), wideband code division multiple access(W-CDMA), code division multiple access (CDMA), time division multipleaccess (TDMA), Bluetooth, Bluetooth Low Energy (BTLE), Wireless Fidelity(Wi-Fi) (e.g., IEEE 802.11a, IEEE 802.11b, IEEE 802.11g, IEEE 802.11n,and/or IEEE 802.11ac), voice over Internet Protocol (VoIP), Wi-MAX, aprotocol for e-mail (e.g., Internet message access protocol (IMAP)and/or post office protocol (POP)), instant messaging (e.g., extensiblemessaging and presence protocol (XMPP), Session Initiation Protocol forInstant Messaging and Presence Leveraging Extensions (SIMPLE), InstantMessaging and Presence Service (IMPS)), and/or Short Message Service(SMS), or any other suitable communication protocol, includingcommunication protocols not yet developed as of the filing date of thisdocument.

Audio circuitry 110, speaker 111, and microphone 113 provide an audiointerface between a user and device 100. Audio circuitry 110 receivesaudio data from peripherals interface 118, converts the audio data to anelectrical signal, and transmits the electrical signal to speaker 111.Speaker 111 converts the electrical signal to human-audible sound waves.Audio circuitry 110 also receives electrical signals converted bymicrophone 113 from sound waves. Audio circuitry 110 converts theelectrical signal to audio data and transmits the audio data toperipherals interface 118 for processing. Audio data is, optionally,retrieved from and/or transmitted to memory 102 and/or RF circuitry 108by peripherals interface 118. In some embodiments, audio circuitry 110also includes a headset jack (e.g., 212, FIG. 2 ). The headset jackprovides an interface between audio circuitry 110 and removable audioinput/output peripherals, such as output-only headphones or a headsetwith both output (e.g., a headphone for one or both ears) and input(e.g., a microphone).

I/O subsystem 106 couples input/output peripherals on device 100, suchas touch screen 112 and other input control devices 116, to peripheralsinterface 118. I/O subsystem 106 optionally includes display controller156, optical sensor controller 158, depth camera controller 169,intensity sensor controller 159, haptic feedback controller 161, and oneor more input controllers 160 for other input or control devices. Theone or more input controllers 160 receive/send electrical signalsfrom/to other input control devices 116. The other input control devices116 optionally include physical buttons (e.g., push buttons, rockerbuttons, etc.), dials, slider switches, joysticks, click wheels, and soforth. In some embodiments, input controller(s) 160 are, optionally,coupled to any (or none) of the following: a keyboard, an infrared port,a USB port, and a pointer device such as a mouse. The one or morebuttons (e.g., 208, FIG. 2 ) optionally include an up/down button forvolume control of speaker 111 and/or microphone 113. The one or morebuttons optionally include a push button (e.g., 206, FIG. 2 ). In someembodiments, the electronic device is a computer system that is incommunication (e.g., via wireless communication, via wiredcommunication) with one or more input devices. In some embodiments, theone or more input devices include a touch-sensitive surface (e.g., atrackpad, as part of a touch-sensitive display). In some embodiments,the one or more input devices include one or more camera sensors (e.g.,one or more optical sensors 164 and/or one or more depth camera sensors175), such as for tracking a user's gestures (e.g., hand gestures and/orair gestures) as input. In some embodiments, the one or more inputdevices are integrated with the computer system. In some embodiments,the one or more input devices are separate from the computer system. Insome embodiments, an air gesture is a gesture that is detected withoutthe user touching an input element that is part of the device (orindependently of an input element that is a part of the device) and isbased on detected motion of a portion of the user's body through the airincluding motion of the user's body relative to an absolute reference(e.g., an angle of the user's arm relative to the ground or a distanceof the user's hand relative to the ground), relative to another portionof the user's body (e.g., movement of a hand of the user relative to ashoulder of the user, movement of one hand of the user relative toanother hand of the user, and/or movement of a finger of the userrelative to another finger or portion of a hand of the user), and/orabsolute motion of a portion of the user's body (e.g., a tap gesturethat includes movement of a hand in a predetermined pose by apredetermined amount and/or speed, or a shake gesture that includes apredetermined speed or amount of rotation of a portion of the user'sbody).

A quick press of the push button optionally disengages a lock of touchscreen 112 or optionally begins a process that uses gestures on thetouch screen to unlock the device, as described in U.S. patentapplication Ser. No. 11/322,549, “Unlocking a Device by PerformingGestures on an Unlock Image,” filed Dec. 23, 2005, U.S. Pat. No.7,657,849, which is hereby incorporated by reference in its entirety. Alonger press of the push button (e.g., 206) optionally turns power todevice 100 on or off. The functionality of one or more of the buttonsare, optionally, user-customizable. Touch screen 112 is used toimplement virtual or soft buttons and one or more soft keyboards.

Touch-sensitive display 112 provides an input interface and an outputinterface between the device and a user. Display controller 156 receivesand/or sends electrical signals from/to touch screen 112. Touch screen112 displays visual output to the user. The visual output optionallyincludes graphics, text, icons, video, and any combination thereof(collectively termed “graphics”). In some embodiments, some or all ofthe visual output optionally corresponds to user-interface objects.

Touch screen 112 has a touch-sensitive surface, sensor, or set ofsensors that accepts input from the user based on haptic and/or tactilecontact. Touch screen 112 and display controller 156 (along with anyassociated modules and/or sets of instructions in memory 102) detectcontact (and any movement or breaking of the contact) on touch screen112 and convert the detected contact into interaction withuser-interface objects (e.g., one or more soft keys, icons, web pages,or images) that are displayed on touch screen 112. In an exemplaryembodiment, a point of contact between touch screen 112 and the usercorresponds to a finger of the user.

Touch screen 112 optionally uses LCD (liquid crystal display)technology, LPD (light emitting polymer display) technology, or LED(light emitting diode) technology, although other display technologiesare used in other embodiments. Touch screen 112 and display controller156 optionally detect contact and any movement or breaking thereof usingany of a plurality of touch sensing technologies now known or laterdeveloped, including but not limited to capacitive, resistive, infrared,and surface acoustic wave technologies, as well as other proximitysensor arrays or other elements for determining one or more points ofcontact with touch screen 112. In an exemplary embodiment, projectedmutual capacitance sensing technology is used, such as that found in theiPhone® and iPod Touch® from Apple Inc. of Cupertino, California.

A touch-sensitive display in some embodiments of touch screen 112 is,optionally, analogous to the multi-touch sensitive touchpads describedin the following U.S. Pat. No. 6,323,846 (Westerman et al.), U.S. Pat.No. 6,570,557 (Westerman et al.), and/or U.S. Pat. No. 6,677,932(Westerman), and/or U.S. Patent Publication 2002/0015024A1, each ofwhich is hereby incorporated by reference in its entirety. However,touch screen 112 displays visual output from device 100, whereastouch-sensitive touchpads do not provide visual output.

A touch-sensitive display in some embodiments of touch screen 112 isdescribed in the following applications: (1) U.S. patent applicationSer. No. 11/381,313, “Multipoint Touch Surface Controller,” filed May 2,2006; (2) U.S. patent application Ser. No. 10/840,862, “MultipointTouchscreen,” filed May 6, 2004; (3) U.S. patent application Ser. No.10/903,964, “Gestures For Touch Sensitive Input Devices,” filed Jul. 30,2004; (4) U.S. patent application Ser. No. 11/048,264, “Gestures ForTouch Sensitive Input Devices,” filed Jan. 31, 2005; (5) U.S. patentapplication Ser. No. 11/038,590, “Mode-Based Graphical User InterfacesFor Touch Sensitive Input Devices,” filed Jan. 18, 2005; (6) U.S. patentapplication Ser. No. 11/228,758, “Virtual Input Device Placement On ATouch Screen User Interface,” filed Sep. 16, 2005; (7) U.S. patentapplication Ser. No. 11/228,700, “Operation Of A Computer With A TouchScreen Interface,” filed Sep. 16, 2005; (8) U.S. patent application Ser.No. 11/228,737, “Activating Virtual Keys Of A Touch-Screen VirtualKeyboard,” filed Sep. 16, 2005; and (9) U.S. patent application Ser. No.11/367,749, “Multi-Functional Hand-Held Device,” filed Mar. 3, 2006. Allof these applications are incorporated by reference herein in theirentirety.

Touch screen 112 optionally has a video resolution in excess of 100 dpi.In some embodiments, the touch screen has a video resolution ofapproximately 160 dpi. The user optionally makes contact with touchscreen 112 using any suitable object or appendage, such as a stylus, afinger, and so forth. In some embodiments, the user interface isdesigned to work primarily with finger-based contacts and gestures,which can be less precise than stylus-based input due to the larger areaof contact of a finger on the touch screen. In some embodiments, thedevice translates the rough finger-based input into a precisepointer/cursor position or command for performing the actions desired bythe user.

In some embodiments, in addition to the touch screen, device 100optionally includes a touchpad for activating or deactivating particularfunctions. In some embodiments, the touchpad is a touch-sensitive areaof the device that, unlike the touch screen, does not display visualoutput. The touchpad is, optionally, a touch-sensitive surface that isseparate from touch screen 112 or an extension of the touch-sensitivesurface formed by the touch screen.

Device 100 also includes power system 162 for powering the variouscomponents. Power system 162 optionally includes a power managementsystem, one or more power sources (e.g., battery, alternating current(AC)), a recharging system, a power failure detection circuit, a powerconverter or inverter, a power status indicator (e.g., a light-emittingdiode (LED)) and any other components associated with the generation,management and distribution of power in portable devices.

Device 100 optionally also includes one or more optical sensors 164.FIG. 1A shows an optical sensor coupled to optical sensor controller 158in I/O subsystem 106. Optical sensor 164 optionally includescharge-coupled device (CCD) or complementary metal-oxide semiconductor(CMOS) phototransistors. Optical sensor 164 receives light from theenvironment, projected through one or more lenses, and converts thelight to data representing an image. In conjunction with imaging module143 (also called a camera module), optical sensor 164 optionallycaptures still images or video. In some embodiments, an optical sensoris located on the back of device 100, opposite touch screen display 112on the front of the device so that the touch screen display is enabledfor use as a viewfinder for still and/or video image acquisition. Insome embodiments, an optical sensor is located on the front of thedevice so that the user's image is, optionally, obtained for videoconferencing while the user views the other video conferenceparticipants on the touch screen display. In some embodiments, theposition of optical sensor 164 can be changed by the user (e.g., byrotating the lens and the sensor in the device housing) so that a singleoptical sensor 164 is used along with the touch screen display for bothvideo conferencing and still and/or video image acquisition.

Device 100 optionally also includes one or more depth camera sensors175. FIG. 1A shows a depth camera sensor coupled to depth cameracontroller 169 in I/O subsystem 106. Depth camera sensor 175 receivesdata from the environment to create a three dimensional model of anobject (e.g., a face) within a scene from a viewpoint (e.g., a depthcamera sensor). In some embodiments, in conjunction with imaging module143 (also called a camera module), depth camera sensor 175 is optionallyused to determine a depth map of different portions of an image capturedby the imaging module 143. In some embodiments, a depth camera sensor islocated on the front of device 100 so that the user's image with depthinformation is, optionally, obtained for video conferencing while theuser views the other video conference participants on the touch screendisplay and to capture selfies with depth map data. In some embodiments,the depth camera sensor 175 is located on the back of device, or on theback and the front of the device 100. In some embodiments, the positionof depth camera sensor 175 can be changed by the user (e.g., by rotatingthe lens and the sensor in the device housing) so that a depth camerasensor 175 is used along with the touch screen display for both videoconferencing and still and/or video image acquisition.

Device 100 optionally also includes one or more contact intensitysensors 165. FIG. 1A shows a contact intensity sensor coupled tointensity sensor controller 159 in I/O subsystem 106. Contact intensitysensor 165 optionally includes one or more piezoresistive strain gauges,capacitive force sensors, electric force sensors, piezoelectric forcesensors, optical force sensors, capacitive touch-sensitive surfaces, orother intensity sensors (e.g., sensors used to measure the force (orpressure) of a contact on a touch-sensitive surface). Contact intensitysensor 165 receives contact intensity information (e.g., pressureinformation or a proxy for pressure information) from the environment.In some embodiments, at least one contact intensity sensor is collocatedwith, or proximate to, a touch-sensitive surface (e.g., touch-sensitivedisplay system 112). In some embodiments, at least one contact intensitysensor is located on the back of device 100, opposite touch screendisplay 112, which is located on the front of device 100.

Device 100 optionally also includes one or more proximity sensors 166.FIG. 1A shows proximity sensor 166 coupled to peripherals interface 118.Alternately, proximity sensor 166 is, optionally, coupled to inputcontroller 160 in I/O subsystem 106. Proximity sensor 166 optionallyperforms as described in U.S. patent application Ser. No. 11/241,839,“Proximity Detector In Handheld Device”; Ser. No. 11/240,788, “ProximityDetector In Handheld Device”; Ser. No. 11/620,702, “Using Ambient LightSensor To Augment Proximity Sensor Output”; Ser. No. 11/586,862,“Automated Response To And Sensing Of User Activity In PortableDevices”; and Ser. No. 11/638,251, “Methods And Systems For AutomaticConfiguration Of Peripherals,” which are hereby incorporated byreference in their entirety. In some embodiments, the proximity sensorturns off and disables touch screen 112 when the multifunction device isplaced near the user's ear (e.g., when the user is making a phone call).

Device 100 optionally also includes one or more tactile outputgenerators 167. FIG. 1A shows a tactile output generator coupled tohaptic feedback controller 161 in I/O subsystem 106. Tactile outputgenerator 167 optionally includes one or more electroacoustic devicessuch as speakers or other audio components and/or electromechanicaldevices that convert energy into linear motion such as a motor,solenoid, electroactive polymer, piezoelectric actuator, electrostaticactuator, or other tactile output generating component (e.g., acomponent that converts electrical signals into tactile outputs on thedevice). Contact intensity sensor 165 receives tactile feedbackgeneration instructions from haptic feedback module 133 and generatestactile outputs on device 100 that are capable of being sensed by a userof device 100. In some embodiments, at least one tactile outputgenerator is collocated with, or proximate to, a touch-sensitive surface(e.g., touch-sensitive display system 112) and, optionally, generates atactile output by moving the touch-sensitive surface vertically (e.g.,in/out of a surface of device 100) or laterally (e.g., back and forth inthe same plane as a surface of device 100). In some embodiments, atleast one tactile output generator sensor is located on the back ofdevice 100, opposite touch screen display 112, which is located on thefront of device 100.

Device 100 optionally also includes one or more accelerometers 168. FIG.1A shows accelerometer 168 coupled to peripherals interface 118.Alternately, accelerometer 168 is, optionally, coupled to an inputcontroller 160 in I/O subsystem 106. Accelerometer 168 optionallyperforms as described in U.S. Patent Publication No. 20050190059,“Acceleration-based Theft Detection System for Portable ElectronicDevices,” and U.S. Patent Publication No. 20060017692, “Methods AndApparatuses For Operating A Portable Device Based On An Accelerometer,”both of which are incorporated by reference herein in their entirety. Insome embodiments, information is displayed on the touch screen displayin a portrait view or a landscape view based on an analysis of datareceived from the one or more accelerometers. Device 100 optionallyincludes, in addition to accelerometer(s) 168, a magnetometer and a GPS(or GLONASS or other global navigation system) receiver for obtaininginformation concerning the location and orientation (e.g., portrait orlandscape) of device 100.

In some embodiments, the software components stored in memory 102include operating system 126, communication module (or set ofinstructions) 128, contact/motion module (or set of instructions) 130,graphics module (or set of instructions) 132, text input module (or setof instructions) 134, Global Positioning System (GPS) module (or set ofinstructions) 135, and applications (or sets of instructions) 136.Furthermore, in some embodiments, memory 102 (FIG. 1A) or 370 (FIG. 3 )stores device/global internal state 157, as shown in FIGS. 1A and 3 .Device/global internal state 157 includes one or more of: activeapplication state, indicating which applications, if any, are currentlyactive; display state, indicating what applications, views or otherinformation occupy various regions of touch screen display 112; sensorstate, including information obtained from the device's various sensorsand input control devices 116; and location information concerning thedevice's location and/or attitude.

Operating system 126 (e.g., Darwin, RTXC, LINUX, UNIX, OS X, iOS,WINDOWS, or an embedded operating system such as VxWorks) includesvarious software components and/or drivers for controlling and managinggeneral system tasks (e.g., memory management, storage device control,power management, etc.) and facilitates communication between varioushardware and software components.

Communication module 128 facilitates communication with other devicesover one or more external ports 124 and also includes various softwarecomponents for handling data received by RF circuitry 108 and/orexternal port 124. External port 124 (e.g., Universal Serial Bus (USB),FIREWIRE, etc.) is adapted for coupling directly to other devices orindirectly over a network (e.g., the Internet, wireless LAN, etc.). Insome embodiments, the external port is a multi-pin (e.g., 30-pin)connector that is the same as, or similar to and/or compatible with, theconnector used on iPod® (trademark of Apple Inc.) devices.

Contact/motion module 130 optionally detects contact with touch screen112 (in conjunction with display controller 156) and othertouch-sensitive devices (e.g., a touchpad or physical click wheel).Contact/motion module 130 includes various software components forperforming various operations related to detection of contact, such asdetermining if contact has occurred (e.g., detecting a finger-downevent), determining an intensity of the contact (e.g., the force orpressure of the contact or a substitute for the force or pressure of thecontact), determining if there is movement of the contact and trackingthe movement across the touch-sensitive surface (e.g., detecting one ormore finger-dragging events), and determining if the contact has ceased(e.g., detecting a finger-up event or a break in contact).Contact/motion module 130 receives contact data from the touch-sensitivesurface. Determining movement of the point of contact, which isrepresented by a series of contact data, optionally includes determiningspeed (magnitude), velocity (magnitude and direction), and/or anacceleration (a change in magnitude and/or direction) of the point ofcontact. These operations are, optionally, applied to single contacts(e.g., one finger contacts) or to multiple simultaneous contacts (e.g.,“multitouch”/multiple finger contacts). In some embodiments,contact/motion module 130 and display controller 156 detect contact on atouchpad.

In some embodiments, contact/motion module 130 uses a set of one or moreintensity thresholds to determine whether an operation has beenperformed by a user (e.g., to determine whether a user has “clicked” onan icon). In some embodiments, at least a subset of the intensitythresholds are determined in accordance with software parameters (e.g.,the intensity thresholds are not determined by the activation thresholdsof particular physical actuators and can be adjusted without changingthe physical hardware of device 100). For example, a mouse “click”threshold of a trackpad or touch screen display can be set to any of alarge range of predefined threshold values without changing the trackpador touch screen display hardware. Additionally, in some implementations,a user of the device is provided with software settings for adjustingone or more of the set of intensity thresholds (e.g., by adjustingindividual intensity thresholds and/or by adjusting a plurality ofintensity thresholds at once with a system-level click “intensity”parameter).

Contact/motion module 130 optionally detects a gesture input by a user.Different gestures on the touch-sensitive surface have different contactpatterns (e.g., different motions, timings, and/or intensities ofdetected contacts). Thus, a gesture is, optionally, detected bydetecting a particular contact pattern. For example, detecting a fingertap gesture includes detecting a finger-down event followed by detectinga finger-up (liftoff) event at the same position (or substantially thesame position) as the finger-down event (e.g., at the position of anicon). As another example, detecting a finger swipe gesture on thetouch-sensitive surface includes detecting a finger-down event followedby detecting one or more finger-dragging events, and subsequentlyfollowed by detecting a finger-up (liftoff) event.

Graphics module 132 includes various known software components forrendering and displaying graphics on touch screen 112 or other display,including components for changing the visual impact (e.g., brightness,transparency, saturation, contrast, or other visual property) ofgraphics that are displayed. As used herein, the term “graphics”includes any object that can be displayed to a user, including, withoutlimitation, text, web pages, icons (such as user-interface objectsincluding soft keys), digital images, videos, animations, and the like.

In some embodiments, graphics module 132 stores data representinggraphics to be used. Each graphic is, optionally, assigned acorresponding code. Graphics module 132 receives, from applicationsetc., one or more codes specifying graphics to be displayed along with,if necessary, coordinate data and other graphic property data, and thengenerates screen image data to output to display controller 156.

Haptic feedback module 133 includes various software components forgenerating instructions used by tactile output generator(s) 167 toproduce tactile outputs at one or more locations on device 100 inresponse to user interactions with device 100.

Text input module 134, which is, optionally, a component of graphicsmodule 132, provides soft keyboards for entering text in variousapplications (e.g., contacts 137, e-mail 140, IM 141, browser 147, andany other application that needs text input).

GPS module 135 determines the location of the device and provides thisinformation for use in various applications (e.g., to telephone 138 foruse in location-based dialing; to camera 143 as picture/video metadata;and to applications that provide location-based services such as weatherwidgets, local yellow page widgets, and map/navigation widgets).

Applications 136 optionally include the following modules (or sets ofinstructions), or a subset or superset thereof:

-   -   Contacts module 137 (sometimes called an address book or contact        list);    -   Telephone module 138;    -   Video conference module 139;    -   E-mail client module 140;    -   Instant messaging (IM) module 141;    -   Workout support module 142;    -   Camera module 143 for still and/or video images;    -   Image management module 144;    -   Video player module;    -   Music player module;    -   Browser module 147;    -   Calendar module 148;    -   Widget modules 149, which optionally include one or more of:        weather widget 149-1, stocks widget 149-2, calculator widget        149-3, alarm clock widget 149-4, dictionary widget 149-5, and        other widgets obtained by the user, as well as user-created        widgets 149-6;    -   Widget creator module 150 for making user-created widgets 149-6;    -   Search module 151;    -   Video and music player module 152, which merges video player        module and music player module;    -   Notes module 153; Map module 154; and/or Online video module        155.

Examples of other applications 136 that are, optionally, stored inmemory 102 include other word processing applications, other imageediting applications, drawing applications, presentation applications,JAVA-enabled applications, encryption, digital rights management, voicerecognition, and voice replication.

In conjunction with touch screen 112, display controller 156,contact/motion module 130, graphics module 132, and text input module134, contacts module 137 are, optionally, used to manage an address bookor contact list (e.g., stored in application internal state 192 ofcontacts module 137 in memory 102 or memory 370), including: addingname(s) to the address book; deleting name(s) from the address book;associating telephone number(s), e-mail address(es), physicaladdress(es) or other information with a name; associating an image witha name; categorizing and sorting names; providing telephone numbers ore-mail addresses to initiate and/or facilitate communications bytelephone 138, video conference module 139, e-mail 140, or IM 141; andso forth.

In conjunction with RF circuitry 108, audio circuitry 110, speaker 111,microphone 113, touch screen 112, display controller 156, contact/motionmodule 130, graphics module 132, and text input module 134, telephonemodule 138 are optionally, used to enter a sequence of characterscorresponding to a telephone number, access one or more telephonenumbers in contacts module 137, modify a telephone number that has beenentered, dial a respective telephone number, conduct a conversation, anddisconnect or hang up when the conversation is completed. As notedabove, the wireless communication optionally uses any of a plurality ofcommunications standards, protocols, and technologies.

In conjunction with RF circuitry 108, audio circuitry 110, speaker 111,microphone 113, touch screen 112, display controller 156, optical sensor164, optical sensor controller 158, contact/motion module 130, graphicsmodule 132, text input module 134, contacts module 137, and telephonemodule 138, video conference module 139 includes executable instructionsto initiate, conduct, and terminate a video conference between a userand one or more other participants in accordance with user instructions.

In conjunction with RF circuitry 108, touch screen 112, displaycontroller 156, contact/motion module 130, graphics module 132, and textinput module 134, e-mail client module 140 includes executableinstructions to create, send, receive, and manage e-mail in response touser instructions. In conjunction with image management module 144,e-mail client module 140 makes it very easy to create and send e-mailswith still or video images taken with camera module 143.

In conjunction with RF circuitry 108, touch screen 112, displaycontroller 156, contact/motion module 130, graphics module 132, and textinput module 134, the instant messaging module 141 includes executableinstructions to enter a sequence of characters corresponding to aninstant message, to modify previously entered characters, to transmit arespective instant message (for example, using a Short Message Service(SMS) or Multimedia Message Service (MMS) protocol for telephony-basedinstant messages or using XMPP, SIMPLE, or IMPS for Internet-basedinstant messages), to receive instant messages, and to view receivedinstant messages. In some embodiments, transmitted and/or receivedinstant messages optionally include graphics, photos, audio files, videofiles and/or other attachments as are supported in an MMS and/or anEnhanced Messaging Service (EMS). As used herein, “instant messaging”refers to both telephony-based messages (e.g., messages sent using SMSor MMS) and Internet-based messages (e.g., messages sent using XMPP,SIMPLE, or IMPS).

In conjunction with RF circuitry 108, touch screen 112, displaycontroller 156, contact/motion module 130, graphics module 132, textinput module 134, GPS module 135, map module 154, and music playermodule, workout support module 142 includes executable instructions tocreate workouts (e.g., with time, distance, and/or calorie burninggoals); communicate with workout sensors (sports devices); receiveworkout sensor data; calibrate sensors used to monitor a workout; selectand play music for a workout; and display, store, and transmit workoutdata.

In conjunction with touch screen 112, display controller 156, opticalsensor(s) 164, optical sensor controller 158, contact/motion module 130,graphics module 132, and image management module 144, camera module 143includes executable instructions to capture still images or video(including a video stream) and store them into memory 102, modifycharacteristics of a still image or video, or delete a still image orvideo from memory 102.

In conjunction with touch screen 112, display controller 156,contact/motion module 130, graphics module 132, text input module 134,and camera module 143, image management module 144 includes executableinstructions to arrange, modify (e.g., edit), or otherwise manipulate,label, delete, present (e.g., in a digital slide show or album), andstore still and/or video images.

In conjunction with RF circuitry 108, touch screen 112, displaycontroller 156, contact/motion module 130, graphics module 132, and textinput module 134, browser module 147 includes executable instructions tobrowse the Internet in accordance with user instructions, includingsearching, linking to, receiving, and displaying web pages or portionsthereof, as well as attachments and other files linked to web pages.

In conjunction with RF circuitry 108, touch screen 112, displaycontroller 156, contact/motion module 130, graphics module 132, textinput module 134, e-mail client module 140, and browser module 147,calendar module 148 includes executable instructions to create, display,modify, and store calendars and data associated with calendars (e.g.,calendar entries, to-do lists, etc.) in accordance with userinstructions.

In conjunction with RF circuitry 108, touch screen 112, displaycontroller 156, contact/motion module 130, graphics module 132, textinput module 134, and browser module 147, widget modules 149 aremini-applications that are, optionally, downloaded and used by a user(e.g., weather widget 149-1, stocks widget 149-2, calculator widget149-3, alarm clock widget 149-4, and dictionary widget 149-5) or createdby the user (e.g., user-created widget 149-6). In some embodiments, awidget includes an HTML (Hypertext Markup Language) file, a CSS(Cascading Style Sheets) file, and a JavaScript file. In someembodiments, a widget includes an XML (Extensible Markup Language) fileand a JavaScript file (e.g., Yahoo! Widgets).

In conjunction with RF circuitry 108, touch screen 112, displaycontroller 156, contact/motion module 130, graphics module 132, textinput module 134, and browser module 147, the widget creator module 150are, optionally, used by a user to create widgets (e.g., turning auser-specified portion of a web page into a widget).

In conjunction with touch screen 112, display controller 156,contact/motion module 130, graphics module 132, and text input module134, search module 151 includes executable instructions to search fortext, music, sound, image, video, and/or other files in memory 102 thatmatch one or more search criteria (e.g., one or more user-specifiedsearch terms) in accordance with user instructions.

In conjunction with touch screen 112, display controller 156,contact/motion module 130, graphics module 132, audio circuitry 110,speaker 111, RF circuitry 108, and browser module 147, video and musicplayer module 152 includes executable instructions that allow the userto download and play back recorded music and other sound files stored inone or more file formats, such as MP3 or AAC files, and executableinstructions to display, present, or otherwise play back videos (e.g.,on touch screen 112 or on an external, connected display via externalport 124). In some embodiments, device 100 optionally includes thefunctionality of an MP3 player, such as an iPod (trademark of AppleInc.).

In conjunction with touch screen 112, display controller 156,contact/motion module 130, graphics module 132, and text input module134, notes module 153 includes executable instructions to create andmanage notes, to-do lists, and the like in accordance with userinstructions.

In conjunction with RF circuitry 108, touch screen 112, displaycontroller 156, contact/motion module 130, graphics module 132, textinput module 134, GPS module 135, and browser module 147, map module 154are, optionally, used to receive, display, modify, and store maps anddata associated with maps (e.g., driving directions, data on stores andother points of interest at or near a particular location, and otherlocation-based data) in accordance with user instructions.

In conjunction with touch screen 112, display controller 156,contact/motion module 130, graphics module 132, audio circuitry 110,speaker 111, RF circuitry 108, text input module 134, e-mail clientmodule 140, and browser module 147, online video module 155 includesinstructions that allow the user to access, browse, receive (e.g., bystreaming and/or download), play back (e.g., on the touch screen or onan external, connected display via external port 124), send an e-mailwith a link to a particular online video, and otherwise manage onlinevideos in one or more file formats, such as H.264. In some embodiments,instant messaging module 141, rather than e-mail client module 140, isused to send a link to a particular online video. Additional descriptionof the online video application can be found in U.S. Provisional PatentApplication No. 60/936,562, “Portable Multifunction Device, Method, andGraphical User Interface for Playing Online Videos,” filed Jun. 20,2007, and U.S. patent application Ser. No. 11/968,067, “PortableMultifunction Device, Method, and Graphical User Interface for PlayingOnline Videos,” filed Dec. 31, 2007, the contents of which are herebyincorporated by reference in their entirety.

Each of the above-identified modules and applications corresponds to aset of executable instructions for performing one or more functionsdescribed above and the methods described in this application (e.g., thecomputer-implemented methods and other information processing methodsdescribed herein). These modules (e.g., sets of instructions) need notbe implemented as separate software programs (such as computer programs(e.g., including instructions)), procedures, or modules, and thusvarious subsets of these modules are, optionally, combined or otherwiserearranged in various embodiments. For example, video player module is,optionally, combined with music player module into a single module(e.g., video and music player module 152, FIG. 1A). In some embodiments,memory 102 optionally stores a subset of the modules and data structuresidentified above. Furthermore, memory 102 optionally stores additionalmodules and data structures not described above.

In some embodiments, device 100 is a device where operation of apredefined set of functions on the device is performed exclusivelythrough a touch screen and/or a touchpad. By using a touch screen and/ora touchpad as the primary input control device for operation of device100, the number of physical input control devices (such as push buttons,dials, and the like) on device 100 is, optionally, reduced.

The predefined set of functions that are performed exclusively through atouch screen and/or a touchpad optionally include navigation betweenuser interfaces. In some embodiments, the touchpad, when touched by theuser, navigates device 100 to a main, home, or root menu from any userinterface that is displayed on device 100. In such embodiments, a “menubutton” is implemented using a touchpad. In some other embodiments, themenu button is a physical push button or other physical input controldevice instead of a touchpad.

FIG. 1B is a block diagram illustrating exemplary components for eventhandling in accordance with some embodiments. In some embodiments,memory 102 (FIG. 1A) or 370 (FIG. 3 ) includes event sorter 170 (e.g.,in operating system 126) and a respective application 136-1 (e.g., anyof the aforementioned applications 137-151, 155, 380-390).

Event sorter 170 receives event information and determines theapplication 136-1 and application view 191 of application 136-1 to whichto deliver the event information. Event sorter 170 includes eventmonitor 171 and event dispatcher module 174. In some embodiments,application 136-1 includes application internal state 192, whichindicates the current application view(s) displayed on touch-sensitivedisplay 112 when the application is active or executing. In someembodiments, device/global internal state 157 is used by event sorter170 to determine which application(s) is (are) currently active, andapplication internal state 192 is used by event sorter 170 to determineapplication views 191 to which to deliver event information.

In some embodiments, application internal state 192 includes additionalinformation, such as one or more of: resume information to be used whenapplication 136-1 resumes execution, user interface state informationthat indicates information being displayed or that is ready for displayby application 136-1, a state queue for enabling the user to go back toa prior state or view of application 136-1, and a redo/undo queue ofprevious actions taken by the user.

Event monitor 171 receives event information from peripherals interface118. Event information includes information about a sub-event (e.g., auser touch on touch-sensitive display 112, as part of a multi-touchgesture). Peripherals interface 118 transmits information it receivesfrom I/O subsystem 106 or a sensor, such as proximity sensor 166,accelerometer(s) 168, and/or microphone 113 (through audio circuitry110). Information that peripherals interface 118 receives from I/Osubsystem 106 includes information from touch-sensitive display 112 or atouch-sensitive surface.

In some embodiments, event monitor 171 sends requests to the peripheralsinterface 118 at predetermined intervals. In response, peripheralsinterface 118 transmits event information. In other embodiments,peripherals interface 118 transmits event information only when there isa significant event (e.g., receiving an input above a predeterminednoise threshold and/or for more than a predetermined duration).

In some embodiments, event sorter 170 also includes a hit viewdetermination module 172 and/or an active event recognizer determinationmodule 173.

Hit view determination module 172 provides software procedures fordetermining where a sub-event has taken place within one or more viewswhen touch-sensitive display 112 displays more than one view. Views aremade up of controls and other elements that a user can see on thedisplay.

Another aspect of the user interface associated with an application is aset of views, sometimes herein called application views or userinterface windows, in which information is displayed and touch-basedgestures occur. The application views (of a respective application) inwhich a touch is detected optionally correspond to programmatic levelswithin a programmatic or view hierarchy of the application. For example,the lowest level view in which a touch is detected is, optionally,called the hit view, and the set of events that are recognized as properinputs are, optionally, determined based, at least in part, on the hitview of the initial touch that begins a touch-based gesture.

Hit view determination module 172 receives information related tosub-events of a touch-based gesture. When an application has multipleviews organized in a hierarchy, hit view determination module 172identifies a hit view as the lowest view in the hierarchy which shouldhandle the sub-event. In most circumstances, the hit view is the lowestlevel view in which an initiating sub-event occurs (e.g., the firstsub-event in the sequence of sub-events that form an event or potentialevent). Once the hit view is identified by the hit view determinationmodule 172, the hit view typically receives all sub-events related tothe same touch or input source for which it was identified as the hitview.

Active event recognizer determination module 173 determines which viewor views within a view hierarchy should receive a particular sequence ofsub-events. In some embodiments, active event recognizer determinationmodule 173 determines that only the hit view should receive a particularsequence of sub-events. In other embodiments, active event recognizerdetermination module 173 determines that all views that include thephysical location of a sub-event are actively involved views, andtherefore determines that all actively involved views should receive aparticular sequence of sub-events. In other embodiments, even if touchsub-events were entirely confined to the area associated with oneparticular view, views higher in the hierarchy would still remain asactively involved views.

Event dispatcher module 174 dispatches the event information to an eventrecognizer (e.g., event recognizer 180). In embodiments including activeevent recognizer determination module 173, event dispatcher module 174delivers the event information to an event recognizer determined byactive event recognizer determination module 173. In some embodiments,event dispatcher module 174 stores in an event queue the eventinformation, which is retrieved by a respective event receiver 182.

In some embodiments, operating system 126 includes event sorter 170.Alternatively, application 136-1 includes event sorter 170. In yet otherembodiments, event sorter 170 is a stand-alone module, or a part ofanother module stored in memory 102, such as contact/motion module 130.

In some embodiments, application 136-1 includes a plurality of eventhandlers 190 and one or more application views 191, each of whichincludes instructions for handling touch events that occur within arespective view of the application's user interface. Each applicationview 191 of the application 136-1 includes one or more event recognizers180. Typically, a respective application view 191 includes a pluralityof event recognizers 180. In other embodiments, one or more of eventrecognizers 180 are part of a separate module, such as a user interfacekit or a higher level object from which application 136-1 inheritsmethods and other properties. In some embodiments, a respective eventhandler 190 includes one or more of: data updater 176, object updater177, GUI updater 178, and/or event data 179 received from event sorter170. Event handler 190 optionally utilizes or calls data updater 176,object updater 177, or GUI updater 178 to update the applicationinternal state 192. Alternatively, one or more of the application views191 include one or more respective event handlers 190. Also, in someembodiments, one or more of data updater 176, object updater 177, andGUI updater 178 are included in a respective application view 191.

A respective event recognizer 180 receives event information (e.g.,event data 179) from event sorter 170 and identifies an event from theevent information. Event recognizer 180 includes event receiver 182 andevent comparator 184. In some embodiments, event recognizer 180 alsoincludes at least a subset of: metadata 183, and event deliveryinstructions 188 (which optionally include sub-event deliveryinstructions).

Event receiver 182 receives event information from event sorter 170. Theevent information includes information about a sub-event, for example, atouch or a touch movement. Depending on the sub-event, the eventinformation also includes additional information, such as location ofthe sub-event. When the sub-event concerns motion of a touch, the eventinformation optionally also includes speed and direction of thesub-event. In some embodiments, events include rotation of the devicefrom one orientation to another (e.g., from a portrait orientation to alandscape orientation, or vice versa), and the event informationincludes corresponding information about the current orientation (alsocalled device attitude) of the device.

Event comparator 184 compares the event information to predefined eventor sub-event definitions and, based on the comparison, determines anevent or sub-event, or determines or updates the state of an event orsub-event. In some embodiments, event comparator 184 includes eventdefinitions 186. Event definitions 186 contain definitions of events(e.g., predefined sequences of sub-events), for example, event 1(187-1), event 2 (187-2), and others. In some embodiments, sub-events inan event (e.g., 187-1 and/or 187-2) include, for example, touch begin,touch end, touch movement, touch cancellation, and multiple touching. Inone example, the definition for event 1 (187-1) is a double tap on adisplayed object. The double tap, for example, comprises a first touch(touch begin) on the displayed object for a predetermined phase, a firstliftoff (touch end) for a predetermined phase, a second touch (touchbegin) on the displayed object for a predetermined phase, and a secondliftoff (touch end) for a predetermined phase. In another example, thedefinition for event 2 (187-2) is a dragging on a displayed object. Thedragging, for example, comprises a touch (or contact) on the displayedobject for a predetermined phase, a movement of the touch acrosstouch-sensitive display 112, and liftoff of the touch (touch end). Insome embodiments, the event also includes information for one or moreassociated event handlers 190.

In some embodiments, event definitions 186 include a definition of anevent for a respective user-interface object. In some embodiments, eventcomparator 184 performs a hit test to determine which user-interfaceobject is associated with a sub-event. For example, in an applicationview in which three user-interface objects are displayed ontouch-sensitive display 112, when a touch is detected on touch-sensitivedisplay 112, event comparator 184 performs a hit test to determine whichof the three user-interface objects is associated with the touch(sub-event). If each displayed object is associated with a respectiveevent handler 190, the event comparator uses the result of the hit testto determine which event handler 190 should be activated. For example,event comparator 184 selects an event handler associated with thesub-event and the object triggering the hit test.

In some embodiments, the definition for a respective event (187) alsoincludes delayed actions that delay delivery of the event informationuntil after it has been determined whether the sequence of sub-eventsdoes or does not correspond to the event recognizer's event type.

When a respective event recognizer 180 determines that the series ofsub-events do not match any of the events in event definitions 186, therespective event recognizer 180 enters an event impossible, eventfailed, or event ended state, after which it disregards subsequentsub-events of the touch-based gesture. In this situation, other eventrecognizers, if any, that remain active for the hit view continue totrack and process sub-events of an ongoing touch-based gesture.

In some embodiments, a respective event recognizer 180 includes metadata183 with configurable properties, flags, and/or lists that indicate howthe event delivery system should perform sub-event delivery to activelyinvolved event recognizers. In some embodiments, metadata 183 includesconfigurable properties, flags, and/or lists that indicate how eventrecognizers interact, or are enabled to interact, with one another. Insome embodiments, metadata 183 includes configurable properties, flags,and/or lists that indicate whether sub-events are delivered to varyinglevels in the view or programmatic hierarchy.

In some embodiments, a respective event recognizer 180 activates eventhandler 190 associated with an event when one or more particularsub-events of an event are recognized. In some embodiments, a respectiveevent recognizer 180 delivers event information associated with theevent to event handler 190. Activating an event handler 190 is distinctfrom sending (and deferred sending) sub-events to a respective hit view.In some embodiments, event recognizer 180 throws a flag associated withthe recognized event, and event handler 190 associated with the flagcatches the flag and performs a predefined process.

In some embodiments, event delivery instructions 188 include sub-eventdelivery instructions that deliver event information about a sub-eventwithout activating an event handler. Instead, the sub-event deliveryinstructions deliver event information to event handlers associated withthe series of sub-events or to actively involved views. Event handlersassociated with the series of sub-events or with actively involved viewsreceive the event information and perform a predetermined process.

In some embodiments, data updater 176 creates and updates data used inapplication 136-1. For example, data updater 176 updates the telephonenumber used in contacts module 137, or stores a video file used in videoplayer module. In some embodiments, object updater 177 creates andupdates objects used in application 136-1. For example, object updater177 creates a new user-interface object or updates the position of auser-interface object. GUI updater 178 updates the GUI. For example, GUIupdater 178 prepares display information and sends it to graphics module132 for display on a touch-sensitive display.

In some embodiments, event handler(s) 190 includes or has access to dataupdater 176, object updater 177, and GUI updater 178. In someembodiments, data updater 176, object updater 177, and GUI updater 178are included in a single module of a respective application 136-1 orapplication view 191. In other embodiments, they are included in two ormore software modules.

It shall be understood that the foregoing discussion regarding eventhandling of user touches on touch-sensitive displays also applies toother forms of user inputs to operate multifunction devices 100 withinput devices, not all of which are initiated on touch screens. Forexample, mouse movement and mouse button presses, optionally coordinatedwith single or multiple keyboard presses or holds; contact movementssuch as taps, drags, scrolls, etc. on touchpads; pen stylus inputs;movement of the device; oral instructions; detected eye movements;biometric inputs; and/or any combination thereof are optionally utilizedas inputs corresponding to sub-events which define an event to berecognized.

FIG. 2 illustrates a portable multifunction device 100 having a touchscreen 112 in accordance with some embodiments. The touch screenoptionally displays one or more graphics within user interface (UI) 200.In this embodiment, as well as others described below, a user is enabledto select one or more of the graphics by making a gesture on thegraphics, for example, with one or more fingers 202 (not drawn to scalein the figure) or one or more styluses 203 (not drawn to scale in thefigure). In some embodiments, selection of one or more graphics occurswhen the user breaks contact with the one or more graphics. In someembodiments, the gesture optionally includes one or more taps, one ormore swipes (from left to right, right to left, upward and/or downward),and/or a rolling of a finger (from right to left, left to right, upwardand/or downward) that has made contact with device 100. In someimplementations or circumstances, inadvertent contact with a graphicdoes not select the graphic. For example, a swipe gesture that sweepsover an application icon optionally does not select the correspondingapplication when the gesture corresponding to selection is a tap.

Device 100 optionally also include one or more physical buttons, such as“home” or menu button 204. As described previously, menu button 204 is,optionally, used to navigate to any application 136 in a set ofapplications that are, optionally, executed on device 100.Alternatively, in some embodiments, the menu button is implemented as asoft key in a GUI displayed on touch screen 112.

In some embodiments, device 100 includes touch screen 112, menu button204, push button 206 for powering the device on/off and locking thedevice, volume adjustment button(s) 208, subscriber identity module(SIM) card slot 210, headset jack 212, and docking/charging externalport 124. Push button 206 is, optionally, used to turn the power on/offon the device by depressing the button and holding the button in thedepressed state for a predefined time interval; to lock the device bydepressing the button and releasing the button before the predefinedtime interval has elapsed; and/or to unlock the device or initiate anunlock process. In an alternative embodiment, device 100 also acceptsverbal input for activation or deactivation of some functions throughmicrophone 113. Device 100 also, optionally, includes one or morecontact intensity sensors 165 for detecting intensity of contacts ontouch screen 112 and/or one or more tactile output generators 167 forgenerating tactile outputs for a user of device 100.

FIG. 3 is a block diagram of an exemplary multifunction device with adisplay and a touch-sensitive surface in accordance with someembodiments. Device 300 need not be portable. In some embodiments,device 300 is a laptop computer, a desktop computer, a tablet computer,a multimedia player device, a navigation device, an educational device(such as a child's learning toy), a gaming system, or a control device(e.g., a home or industrial controller). Device 300 typically includesone or more processing units (CPUs) 310, one or more network or othercommunications interfaces 360, memory 370, and one or more communicationbuses 320 for interconnecting these components. Communication buses 320optionally include circuitry (sometimes called a chipset) thatinterconnects and controls communications between system components.Device 300 includes input/output (I/O) interface 330 comprising display340, which is typically a touch screen display. I/O interface 330 alsooptionally includes a keyboard and/or mouse (or other pointing device)350 and touchpad 355, tactile output generator 357 for generatingtactile outputs on device 300 (e.g., similar to tactile outputgenerator(s) 167 described above with reference to FIG. 1A), sensors 359(e.g., optical, acceleration, proximity, touch-sensitive, and/or contactintensity sensors similar to contact intensity sensor(s) 165 describedabove with reference to FIG. 1A). Memory 370 includes high-speed randomaccess memory, such as DRAM, SRAM, DDR RAM, or other random access solidstate memory devices; and optionally includes non-volatile memory, suchas one or more magnetic disk storage devices, optical disk storagedevices, flash memory devices, or other non-volatile solid state storagedevices. Memory 370 optionally includes one or more storage devicesremotely located from CPU(s) 310. In some embodiments, memory 370 storesprograms, modules, and data structures analogous to the programs,modules, and data structures stored in memory 102 of portablemultifunction device 100 (FIG. 1A), or a subset thereof. Furthermore,memory 370 optionally stores additional programs, modules, and datastructures not present in memory 102 of portable multifunction device100. For example, memory 370 of device 300 optionally stores drawingmodule 380, presentation module 382, word processing module 384, websitecreation module 386, disk authoring module 388, and/or spreadsheetmodule 390, while memory 102 of portable multifunction device 100 (FIG.1A) optionally does not store these modules.

Each of the above-identified elements in FIG. 3 is, optionally, storedin one or more of the previously mentioned memory devices. Each of theabove-identified modules corresponds to a set of instructions forperforming a function described above. The above-identified modules orcomputer programs (e.g., sets of instructions or including instructions)need not be implemented as separate software programs (such as computerprograms (e.g., including instructions)), procedures, or modules, andthus various subsets of these modules are, optionally, combined orotherwise rearranged in various embodiments. In some embodiments, memory370 optionally stores a subset of the modules and data structuresidentified above. Furthermore, memory 370 optionally stores additionalmodules and data structures not described above.

Attention is now directed towards embodiments of user interfaces thatare, optionally, implemented on, for example, portable multifunctiondevice 100.

FIG. 4A illustrates an exemplary user interface for a menu ofapplications on portable multifunction device 100 in accordance withsome embodiments. Similar user interfaces are, optionally, implementedon device 300. In some embodiments, user interface 400 includes thefollowing elements, or a subset or superset thereof:

-   -   Signal strength indicator(s) 402 for wireless communication(s),        such as cellular and Wi-Fi signals;    -   Time 404;    -   Bluetooth indicator 405;    -   Battery status indicator 406;    -   Tray 408 with icons for frequently used applications, such as:        -   Icon 416 for telephone module 138, labeled “Phone,” which            optionally includes an indicator 414 of the number of missed            calls or voicemail messages;        -   Icon 418 for e-mail client module 140, labeled “Mail,” which            optionally includes an indicator 410 of the number of unread            e-mails;        -   Icon 420 for browser module 147, labeled “Browser;” and        -   Icon 422 for video and music player module 152, also            referred to as iPod (trademark of Apple Inc.) module 152,            labeled “iPod;” and Icons for other applications, such as:        -   Icon 424 for IM module 141, labeled “Messages;”        -   Icon 426 for calendar module 148, labeled “Calendar;”        -   Icon 428 for image management module 144, labeled “Photos;”        -   Icon 430 for camera module 143, labeled “Camera;”        -   Icon 432 for online video module 155, labeled “Online            Video;”        -   Icon 434 for stocks widget 149-2, labeled “Stocks;”        -   Icon 436 for map module 154, labeled “Maps;”        -   Icon 438 for weather widget 149-1, labeled “Weather;”        -   Icon 440 for alarm clock widget 149-4, labeled “Clock;”        -   Icon 442 for workout support module 142, labeled “Workout            Support;”        -   Icon 444 for notes module 153, labeled “Notes;” and        -   Icon 446 for a settings application or module, labeled            “Settings,” which provides access to settings for device 100            and its various applications 136.

It should be noted that the icon labels illustrated in FIG. 4A aremerely exemplary. For example, icon 422 for video and music playermodule 152 is labeled “Music” or “Music Player.” Other labels are,optionally, used for various application icons. In some embodiments, alabel for a respective application icon includes a name of anapplication corresponding to the respective application icon. In someembodiments, a label for a particular application icon is distinct froma name of an application corresponding to the particular applicationicon.

FIG. 4B illustrates an exemplary user interface on a device (e.g.,device 300, FIG. 3 ) with a touch-sensitive surface 451 (e.g., a tabletor touchpad 355, FIG. 3 ) that is separate from the display 450 (e.g.,touch screen display 112). Device 300 also, optionally, includes one ormore contact intensity sensors (e.g., one or more of sensors 359) fordetecting intensity of contacts on touch-sensitive surface 451 and/orone or more tactile output generators 357 for generating tactile outputsfor a user of device 300.

Although some of the examples that follow will be given with referenceto inputs on touch screen display 112 (where the touch-sensitive surfaceand the display are combined), in some embodiments, the device detectsinputs on a touch-sensitive surface that is separate from the display,as shown in FIG. 4B. In some embodiments, the touch-sensitive surface(e.g., 451 in FIG. 4B) has a primary axis (e.g., 452 in FIG. 4B) thatcorresponds to a primary axis (e.g., 453 in FIG. 4B) on the display(e.g., 450). In accordance with these embodiments, the device detectscontacts (e.g., 460 and 462 in FIG. 4B) with the touch-sensitive surface451 at locations that correspond to respective locations on the display(e.g., in FIG. 4B, 460 corresponds to 468 and 462 corresponds to 470).In this way, user inputs (e.g., contacts 460 and 462, and movementsthereof) detected by the device on the touch-sensitive surface (e.g.,451 in FIG. 4B) are used by the device to manipulate the user interfaceon the display (e.g., 450 in FIG. 4B) of the multifunction device whenthe touch-sensitive surface is separate from the display. It should beunderstood that similar methods are, optionally, used for other userinterfaces described herein.

Additionally, while the following examples are given primarily withreference to finger inputs (e.g., finger contacts, finger tap gestures,finger swipe gestures), it should be understood that, in someembodiments, one or more of the finger inputs are replaced with inputfrom another input device (e.g., a mouse-based input or stylus input).For example, a swipe gesture is, optionally, replaced with a mouse click(e.g., instead of a contact) followed by movement of the cursor alongthe path of the swipe (e.g., instead of movement of the contact). Asanother example, a tap gesture is, optionally, replaced with a mouseclick while the cursor is located over the location of the tap gesture(e.g., instead of detection of the contact followed by ceasing to detectthe contact). Similarly, when multiple user inputs are simultaneouslydetected, it should be understood that multiple computer mice are,optionally, used simultaneously, or a mouse and finger contacts are,optionally, used simultaneously.

FIG. 5A illustrates exemplary personal electronic device 500. Device 500includes body 502. In some embodiments, device 500 can include some orall of the features described with respect to devices 100 and 300 (e.g.,FIGS. 1A-4B). In some embodiments, device 500 has touch-sensitivedisplay screen 504, hereafter touch screen 504. Alternatively, or inaddition to touch screen 504, device 500 has a display and atouch-sensitive surface. As with devices 100 and 300, in someembodiments, touch screen 504 (or the touch-sensitive surface)optionally includes one or more intensity sensors for detectingintensity of contacts (e.g., touches) being applied. The one or moreintensity sensors of touch screen 504 (or the touch-sensitive surface)can provide output data that represents the intensity of touches. Theuser interface of device 500 can respond to touches based on theirintensity, meaning that touches of different intensities can invokedifferent user interface operations on device 500.

Exemplary techniques for detecting and processing touch intensity arefound, for example, in related applications: International PatentApplication Serial No. PCT/US2013/040061, titled “Device, Method, andGraphical User Interface for Displaying User Interface ObjectsCorresponding to an application,” filed May 8, 2013, published as WIPOPublication No. WO/2013/169849, and International Patent ApplicationSerial No. PCT/US2013/069483, titled “Device, Method, and Graphical UserInterface for Transitioning Between Touch Input to Display OutputRelationships,” filed Nov. 11, 2013, published as WIPO Publication No.WO/2014/105276, each of which is hereby incorporated by reference intheir entirety.

In some embodiments, device 500 has one or more input mechanisms 506 and508. Input mechanisms 506 and 508, if included, can be physical.Examples of physical input mechanisms include push buttons and rotatablemechanisms. In some embodiments, device 500 has one or more attachmentmechanisms. Such attachment mechanisms, if included, can permitattachment of device 500 with, for example, hats, eyewear, earrings,necklaces, shirts, jackets, bracelets, watch straps, chains, trousers,belts, shoes, purses, backpacks, and so forth. These attachmentmechanisms permit device 500 to be worn by a user.

FIG. 5B depicts exemplary personal electronic device 500. In someembodiments, device 500 can include some or all of the componentsdescribed with respect to FIGS. 1A, 1B, and 3. Device 500 has bus 512that operatively couples I/O section 514 with one or more computerprocessors 516 and memory 518. I/O section 514 can be connected todisplay 504, which can have touch-sensitive component 522 and,optionally, intensity sensor 524 (e.g., contact intensity sensor). Inaddition, I/O section 514 can be connected with communication unit 530for receiving application and operating system data, using Wi-Fi,Bluetooth, near field communication (NFC), cellular, and/or otherwireless communication techniques. Device 500 can include inputmechanisms 506 and/or 508. Input mechanism 506 is, optionally, arotatable input device or a depressible and rotatable input device, forexample. Input mechanism 508 is, optionally, a button, in some examples.

Input mechanism 508 is, optionally, a microphone, in some examples.Personal electronic device 500 optionally includes various sensors, suchas GPS sensor 532, accelerometer 534, directional sensor 540 (e.g.,compass), gyroscope 536, motion sensor 538, and/or a combinationthereof, all of which can be operatively connected to I/O section 514.

Memory 518 of personal electronic device 500 can include one or morenon-transitory computer-readable storage mediums, for storingcomputer-executable instructions, which, when executed by one or morecomputer processors 516, for example, can cause the computer processorsto perform the techniques described below, including method 800 (FIGS.8A-8B). A computer-readable storage medium can be any medium that cantangibly contain or store computer-executable instructions for use by orin connection with the instruction execution system, apparatus, ordevice. In some examples, the storage medium is a transitorycomputer-readable storage medium. In some examples, the storage mediumis a non-transitory computer-readable storage medium. The non-transitorycomputer-readable storage medium can include, but is not limited to,magnetic, optical, and/or semiconductor storages. Examples of suchstorage include magnetic disks, optical discs based on CD, DVD, orBlu-ray technologies, as well as persistent solid-state memory such asflash, solid-state drives, and the like. Personal electronic device 500is not limited to the components and configuration of FIG. 5B, but caninclude other or additional components in multiple configurations.

As used here, the term “affordance” refers to a user-interactivegraphical user interface object that is, optionally, displayed on thedisplay screen of devices 100, 300, and/or 500 (FIGS. 1A, 3, and 5A-5B).For example, an image (e.g., icon), a button, and text (e.g., hyperlink)each optionally constitute an affordance.

As used herein, the term “focus selector” refers to an input elementthat indicates a current part of a user interface with which a user isinteracting. In some implementations that include a cursor or otherlocation marker, the cursor acts as a “focus selector” so that when aninput (e.g., a press input) is detected on a touch-sensitive surface(e.g., touchpad 355 in FIG. 3 or touch-sensitive surface 451 in FIG. 4B)while the cursor is over a particular user interface element (e.g., abutton, window, slider, or other user interface element), the particularuser interface element is adjusted in accordance with the detectedinput. In some implementations that include a touch screen display(e.g., touch-sensitive display system 112 in FIG. 1A or touch screen 112in FIG. 4A) that enables direct interaction with user interface elementson the touch screen display, a detected contact on the touch screen actsas a “focus selector” so that when an input (e.g., a press input by thecontact) is detected on the touch screen display at a location of aparticular user interface element (e.g., a button, window, slider, orother user interface element), the particular user interface element isadjusted in accordance with the detected input. In some implementations,focus is moved from one region of a user interface to another region ofthe user interface without corresponding movement of a cursor ormovement of a contact on a touch screen display (e.g., by using a tabkey or arrow keys to move focus from one button to another button); inthese implementations, the focus selector moves in accordance withmovement of focus between different regions of the user interface.Without regard to the specific form taken by the focus selector, thefocus selector is generally the user interface element (or contact on atouch screen display) that is controlled by the user so as tocommunicate the user's intended interaction with the user interface(e.g., by indicating, to the device, the element of the user interfacewith which the user is intending to interact). For example, the locationof a focus selector (e.g., a cursor, a contact, or a selection box) overa respective button while a press input is detected on thetouch-sensitive surface (e.g., a touchpad or touch screen) will indicatethat the user is intending to activate the respective button (as opposedto other user interface elements shown on a display of the device).

As used in the specification and claims, the term “characteristicintensity” of a contact refers to a characteristic of the contact basedon one or more intensities of the contact. In some embodiments, thecharacteristic intensity is based on multiple intensity samples. Thecharacteristic intensity is, optionally, based on a predefined number ofintensity samples, or a set of intensity samples collected during apredetermined time period (e.g., 0.05, 0.1, 0.2, 0.5, 1, 2, 5, 10seconds) relative to a predefined event (e.g., after detecting thecontact, prior to detecting liftoff of the contact, before or afterdetecting a start of movement of the contact, prior to detecting an endof the contact, before or after detecting an increase in intensity ofthe contact, and/or before or after detecting a decrease in intensity ofthe contact). A characteristic intensity of a contact is, optionally,based on one or more of: a maximum value of the intensities of thecontact, a mean value of the intensities of the contact, an averagevalue of the intensities of the contact, a top 10 percentile value ofthe intensities of the contact, a value at the half maximum of theintensities of the contact, a value at the 90 percent maximum of theintensities of the contact, or the like. In some embodiments, theduration of the contact is used in determining the characteristicintensity (e.g., when the characteristic intensity is an average of theintensity of the contact over time). In some embodiments, thecharacteristic intensity is compared to a set of one or more intensitythresholds to determine whether an operation has been performed by auser. For example, the set of one or more intensity thresholdsoptionally includes a first intensity threshold and a second intensitythreshold. In this example, a contact with a characteristic intensitythat does not exceed the first threshold results in a first operation, acontact with a characteristic intensity that exceeds the first intensitythreshold and does not exceed the second intensity threshold results ina second operation, and a contact with a characteristic intensity thatexceeds the second threshold results in a third operation. In someembodiments, a comparison between the characteristic intensity and oneor more thresholds is used to determine whether or not to perform one ormore operations (e.g., whether to perform a respective operation orforgo performing the respective operation), rather than being used todetermine whether to perform a first operation or a second operation.

As used herein, an “installed application” refers to a softwareapplication that has been downloaded onto an electronic device (e.g.,devices 100, 300, and/or 500) and is ready to be launched (e.g., becomeopened) on the device. In some embodiments, a downloaded applicationbecomes an installed application by way of an installation program thatextracts program portions from a downloaded package and integrates theextracted portions with the operating system of the computer system.

FIG. 6 illustrates an exemplary system for updating a language modelbased on user feedback in accordance with some embodiments. In someembodiments, system 600 may be implemented on one or more electronicdevices (e.g., 100, 300, 500, 700) and the components and functions ofsystem 600 may be distributed in any manner between the devices. In someembodiments, system 600 may be implemented on one or more server deviceshaving architectures similar to or the same as devices 100, 300, 500, or700 (e.g., processors, network interfaces, controllers, and memories)but with greater memory, computing, and/or processing resources thandevices 100, 300, 500, or 700. In other embodiments, system 600 may beimplemented according to a client-server architecture, where thecomponents of system 600 may be distributed in any manner between one ormore client devices (e.g., 100, 300, 500, 700) and one or more serverdevices communicatively coupled to the client device(s). The systemsillustrated in these figures are used to illustrate the processesdescribed below, including the processes in FIGS. 8A-8B.

System 600 is implemented using hardware, software, or a combination ofhardware and software to carry out the principles discussed herein.Further, system 600 is exemplary, and thus system 600 can have more orfewer components than shown, can combine two or more components, or canhave a different configuration or arrangement of the components.Further, although the below discussion describes functions beingperformed at a single component of system 600, it is to be understoodthat such functions can be performed at other components of system 600and that such functions can be performed at more than one component ofsystem 600.

System 600 includes input module 602. Input module 602 obtains one ormore input tokens. In some embodiments, each input token may representone or more characters or one or more words (e.g., an individualcharacter, a character sequence, an emoji, a fragment of a word, a word,a fragment of a phrase, an entire phrase, a fragment of a sentence, anentire sentence, and/or the like), one or more phonemes (e.g., forspeech recognition), or one or more spatial coordinates (e.g., forhandwriting recognition). In some embodiments, input module 602 obtainsthe one or more input tokens based on user text input at an electronicdevice (e.g., device 100, 300, 500, or 700). In some embodiments, theuser text input at the electronic device is performed via gesture input(e.g., handwritten input), device keyboard input, speech input (e.g.,using a dictation service), peripheral device input, or a combination orsub-combination thereof. For example, FIG. 7A illustrates user textinput 702A (“Vegan chickn”), displayed in a text input field of anotebook application of device 700. As another example, FIG. 7Cillustrates user text input 702C (“Vegan chick'n for”) displayed in thetext input field of the notebook application of device 700.

System 600 includes initial language model 604. Initial language model604 includes a joint prediction model. In response to receiving the oneor more input tokens from input module 602, the joint prediction modeldetermines (e.g., predicts), based on the one or more input tokens, afirst set of one or more output tokens (e.g., the predicted next textinput based on the preexisting text input) and a corresponding predicteduser action to be performed in response to the first set of one or moreoutput tokens (e.g., in response to the prediction).

Determining (e.g., predicting) the first set of one or more outputtokens includes obtaining a first output token probability distributionY′ drawn from a first overall probability distribution D′ of initiallanguage model 604. In some embodiments, the first output tokenprobability distribution Y′ represents a probability distribution overan underlying token vocabulary of language model 604 at a specific pointin time (e.g., specifically given the one or more input tokens), whilethe first overall probability distribution D′ represents a probabilitydistribution over all such probability distributions (e.g., over time).Accordingly, in these embodiments, the first output token probabilitydistribution Y′ is said to be drawn from the first overall probabilitydistribution D′. In some embodiments, the first output token probabilitydistribution Y′ may be obtained using an encoder-decoder model, whichencodes the one or more input tokens as an input context vector W, anddecodes the input context vector W into the first output tokenprobability distribution Y′. For example, with reference to FIG. 7A, thefirst output token probability distribution Y′ may indicate that, giventhe input context of text input 702A (“Vegan chickn”), the most likelynext text input is predicted token 704A (“chickens”), correcting thetoken “chickn” of text input 702A.

In some embodiments, the joint prediction model further predicts asecond set of one or more output tokens based on the first output tokenprobability distribution Y′. For example, with reference to FIG. 7A, thefirst output token probability distribution Y′ may indicate not onlythat the most likely next text input is predicted token 704A(“chickens”), but also that the second most likely next text input ispredicted token 704B (“chickenpox”), and so forth.

In some embodiments, the first set of one or more output tokensrepresent a predicted replacement for at least one of the one or moreinput tokens, such as a correction or a completion (e.g., of anincomplete word). For example, in FIG. 7A, predicted token 704A(“chickens”) and predicted token 704B (“chickenpox”) are tokenspredicted as corrections to the final word of text input 702A (“Veganchickn”). In some embodiments, the first set of one or more outputtokens represent predicted next tokens (e.g., tokens following the oneor more input tokens). For example, in FIG. 7C, predicted tokens 704C(“lunch”), 704D (“dinner”), and 704E (“the”) are tokens predicted tofollow text input 702C (“Vegan chick'n for”).

In some embodiments, the joint prediction model determines (e.g.,predicts) the corresponding predicted user action using an actionclassifier. In some embodiments, the corresponding predicted user actionmay be a user action accepting the first set of one or more outputtokens or a user action not accepting the first set of one or moreoutput tokens (e.g., a prediction that the user will reject the set ofone or more output tokens, a prediction that the user will accept adifferent set of one or more output tokens, etc.) For example, in FIG.7A, the predicted user action may be a prediction that the user willaccept predicted token 704A (“chickens”) as a correction to the finalword of text input 702A (“Vegan chickn”). As another example, in FIG.7C, the predicted user action may be a prediction that the user will notaccept predicted token 704C (“lunch”) as the next token following textinput 702C (“Vegan chick'n for”), but will instead accept predictedtoken 704D (“dinner”), or a prediction that the user will not accept anyof predicted tokens 704C (“lunch”), 704D (“dinner”), or 704E (“the”).

In some embodiments, initial language model 604 includes an n-grammodel. In some embodiments, initial language model 604 includes a neuralnetwork-based model (e.g., a self-attentive neural network based model,a recurrent neural network (RNN)-based model, a long short term memory(LSTM)-based model, an LSTM-based model with attention, a gatedrecurrent unit (GRU)-based model, transformer-based models (e.g.,vanilla transformer), an XLNet-based model, and so forth).

System 600 includes output module 606. Output module 606 provides anoutput including at least the first set of one or more output tokens.For example, as illustrated in FIG. 7A, providing the output includesdisplaying a set of affordances indicating predicted token 704A(“chickens”) and predicted token 704D (“chickenpox”). As predictedtokens 704A and 704B represent predicted corrections to text input 702A(“Vegan chickn”), if a user were to select one of the affordances, atleast a portion of text input 702A would be replaced with thecorresponding predicted token in the text input field. Accordingly, asillustrated in FIG. 7A, providing the output may also include displayingan indication of the portion of text input 702A (e.g., the highlightingaround the portion “chickn”) that would be replaced with the predictedtoken(s) if the user selects one of the predictions. As another example,as illustrated in FIG. 7C, providing the output includes displaying aset of selectable affordances indicating predicted token 704C (“lunch”),predicted token 704D (“dinner”), and predicted token 704E (“the”). Aspredicted tokens 704C, 704D, and 704E represent predicted next tokensfollowing text input 702C (“Vegan chick'n for”), if a user were toselect one of the affordances, the corresponding predicted token wouldbe inserted following text input 702C in the text input field.

System 600 includes feedback module 608. After output module 606provides the output including the first set of one or more outputtokens, feedback module 608 detects a first user action responding tothe first set of one or more output tokens. For example, as shown inFIG. 7B, rather than selecting one of the affordances indicatingpredicted tokens 704A or 704B, the user manually corrects text input702A, deleting the portion “chickn” and replacing it with text input702B, “chick'n” in the text input field. Accordingly, feedback module608 detects that the user has taken an action rejecting the predictedcorrections (e.g., an action that does not accept the first set of oneor more output tokens). As another example, as shown in FIG. 7D, ratherthan selecting one of the affordances indicating predicted tokens 704C,704D, or 704E (e.g., the predicted next text), the user manually enterstext input 702D, “kebabs,” following text input 702C in the text inputfield. Accordingly, feedback module 608 detects that the user has takenan action rejecting the predicted next text (e.g., an action that doesnot accept the first set of one or more output tokens). In someembodiments, based on the detected first user action, feedback module608 determines one or more user-validated tokens. For example, auser-validated token is a token approved of or provided by the user,such as a token parsed from text input 702B (“chick'n”) of FIG. 7B or atoken parsed from text input 702D (“kebabs”) of FIG. 7D.

In response to detecting the user action responding to the first set ofone or more output tokens, feedback module 608 determines whether thefirst user action matches the predicted user action (e.g., the predictedresponse to the first set of one or more tokens). For example, referringto FIGS. 7A-7B, if the predicted user action was an acceptance ofpredicted token 704A (“chickens”) as a correction to text input 702A(“Vegan chickn”), the first user action rejecting the predictedcorrections illustrated in FIG. 7B does not match the predicted action.As another example, referring to FIGS. 7C-7D, if the predicted useraction was a rejection of predicted token 704C (“lunch”) as the nexttoken following text input 702B (“Vegan chick'n for”), but an acceptanceof predicted token 704D (“dinner”), the first user action rejecting allof the predicted next tokens illustrated in FIG. 7D does not match thepredicted user action.

System 600 includes updating module 610. If feedback module 608determines that the first user action does not match the predicted useraction, updating module 610 initiates an update of initial languagemodel 604, based on the user feedback data (e.g., the first user actionand/or the user-validated token), to obtain updated language model 620.For example, a mismatch between the predicted user action and the firstuser action can automatically trigger an update to the language modelused for text prediction in order to incorporate the user feedback dataas it is received.

As discussed above, initial language model 604 includes a jointprediction model, which predicts tokens using first overall probabilitydistribution D′ and user actions using an action classifier.Accordingly, in order to update the action prediction of initiallanguage model 604, in some embodiments, updating module 610 implementsa reinforcement learning model to modify an action-prediction parameterof the action classifier of the joint prediction model for updatedlanguage module 620 based on the mismatch between the first user actionand the detected first action. Likewise, in order to update the tokenprediction of initial language model 604, in some embodiments, updatingmodule 610 implements a generative adversarial network (GAN) to generateupdated overall probability distribution D″ of updated language model620 by iteratively updating initial language model 604.

In embodiments implementing a GAN, system 600 includes target languagemodel 612, which is used to constrain the update of initial languagemodel 604 using the GAN. Like initial language model 604, targetlanguage model 612 includes a second overall token probabilitydistribution D (e.g., representing an output probability distributionsover all discrete output probability distributions, as discussed abovewith respect to the first overall probability distribution D′). In someembodiments, target language model 612 has been trained on a set of userdata. For example, the set of user data may include data generated bythe user (e.g., text input by the user) and/or data associated with theuser (e.g., text the user has received, read, or otherwise interactedwith). Accordingly, by using target language model 612 to constrain theupdate of initial language model 604 using the GAN, the user data isreflected in updated language module 620. In some embodiments, targetlanguage model 612 has been trained on a set of general training data,such as a very large corpus of text samples used to train languagemodels in accordance with general usage (e.g., rather than for aspecific user).

In these embodiments, updating module 610 includes generator 614 of theGAN, which iteratively updates first overall probability distribution D′to ultimately obtain updated overall probability distribution D″. Forexample, generator 614 can be initialized with initial language model604/first overall probability distribution D′, such that, for an initialiteration of the update, a generated output token probabilitydistribution

(W) is equivalent to the first output token probability distribution Y′drawn from the first overall probability distribution D′ given the inputcontext vector W (e.g., for the initial iteration,

(W)=Y′).

In these embodiments, updating module 610 further includes adjustmentmodule 616. Adjustment module modifies (e.g., adjusts) the generatedoutput token probability distribution

(W), based on the user feedback data (e.g., the first user action and/orthe user-validated token), to obtain a modified output token probabilitydistribution Y″. In order to modify

(W) based on the user feedback data, adjustment module 616 generates asparse output token probability distribution Z based on the one or moreuser-validated tokens determined from the detected first user action.For example, for the user-validated token parsed from text input 702B(“chick'n”) of FIG. 7B, the sparse output token probability distributionZ is a probability distribution over the underlying token vocabulary ofinitial language model 604 that predicts “chick'n” as a correction tothe input context of text input 702A (“Vegan chickn”). Accordingly, forthe initial iteration where

(W)=Y′, the modified output token probability distribution Y″ iscalculated as:

$Y^{''} = {\left( (W) \right) = {\left( Y^{\prime} \right) = \frac{Y^{\prime} + {\lambda \cdot Z}}{{Y^{\prime} + {\lambda \cdot Z}}}}}$

where λ>0 is an optional weight that controls the importance of the userfeedback (e.g., the first user action and the user-validated token)relative to the prediction (e.g., the predicted user action and thefirst set of one or more tokens). The normalization ensures that themodified output token probability distribution Y″ is a properprobability distribution over the underlying token vocabulary of initiallanguage model 604.

In these embodiments, updating module 610 further includes discriminator618. Discriminator 618 is trained to determine a probability

that a given output probability distribution is drawn from the second(e.g., target) overall token probability distribution D. For example,discriminator 618 may be trained to output

(Y)=1 given an output token probability distribution Y known to be drawnfrom the second (e.g., target) overall probability distribution D, andtrained to output

(Y′)=0 given output token probability distribution Y′ known not to bedrawn from D (e.g., for the first output token probability distributionY′ known to be drawn from the first overall probability distribution D′of initial language model 604). A trained discriminator output of 0.5thus indicates an equal probability that the observed output tokenprobability distribution is equally likely to have been drawn from drawnfrom D as it is to have been drawn from a different overall tokenprobability distribution. Accordingly, in some embodiments, updatingmodule 810 may use convergence criteria including a discriminator outputwithin a narrow threshold range of 0.5.

Accordingly, in order to update initial language model 604 based on theuser feedback data (e.g., the first user action and/or theuser-validated token), generator 616 (

) and discriminator 618 (

) are trained jointly by solving:

min max ( , ) = 𝔼 Y ∼ D ⁢ { log [ ( Y ) ] } + 𝔼 [ ( W ) ] ∼ D ″ ⁢ { log [1 - ( [ ( W ) ] ) ] }

where

(

,

) denotes an overall cost function of a minimax two-player game andwhere W represents an input of previous tokens (i.e., an input context).Maximizing

over

while minimizing

over

ensures that, after enough iterations, generator 616 will have updatedthe first language model such that the modified output probabilitydistribution Y″=

[

(W)] (e.g., an output probability distribution adjusted based on theuser feedback data), drawn from updated overall probability distributionD″, produces a probability

(Y″) that falls within a narrow range of 0.5. When

(Y″) falls within a narrow range of 0.5 (e.g., when

(Y″) satisfies convergence criteria), the second output tokenprobability distribution Y″ is probabilistically indistinguishable froman output probability distribution Y drawn from the second (e.g.,target) overall probability distribution D, and thus, the updatedoverall probability distribution D″ is considered to have converged withthe second overall probability distribution D.

In some embodiments, once updating module 610 updates initial languagemodel 604 to obtain updated language model 620, system 600 uses updatedlanguage model 620 for text prediction. For example, upon receivingsubsequent input tokens (e.g., at a text input field of device 700) frominput module 602, the joint prediction model of updated language model620, including the updated action classifier and the updated overallprobability distribution D″, predicts a new set of one or more outputtokens and a corresponding user action to be performed on the new set ofone or more output tokens.

FIGS. 8A-8B illustrate a flow diagram illustrating a method for updatinga language model based on user feedback using a computer system inaccordance with some embodiments. Method 800 is performed at anelectronic device (e.g., 100, 300, 500, 700) with one or more processorsand memory. Some operations in method 800 are, optionally, combined, theorders of some operations are, optionally, changed, and some operationsare, optionally, omitted.

In some embodiments, the electronic device (e.g., 700) is a computersystem. The computer system is optionally in communication (e.g., wiredcommunication, wireless communication) with a display generationcomponent and with one or more input devices. The display generationcomponent is configured to provide visual output, such as display via aCRT display, display via an LED display, or display via imageprojection. In some embodiments, the display generation component isintegrated with the computer system. In some embodiments, the displaygeneration component is separate from the computer system. The one ormore input devices are configured to receive input, such as atouch-sensitive surface receiving user input. In some embodiments, theone or more input devices are integrated with the computer system. Insome embodiments, the one or more input devices are separate from thecomputer system. Thus, the computer system can transmit, via a wired orwireless connection, data (e.g., image data or video data) to anintegrated or external display generation component to visually producethe content (e.g., using a display device) and can receive, a wired orwireless connection, input from the one or more input devices.

As described below, method 800 provides an efficient way to update alanguage model based on user feedback. Efficiently updating a languagemodel used for text prediction (e.g., to accurately reflect a user'sindividual linguistic idiosyncrasies) reduces the cognitive burden on auser for text entry, thereby creating a more efficient human-machineinterface. For battery-operated computing devices, efficiently updatingthe language model and allowing the user to enter text more quickly andaccurately conserves power and increases the time between batterycharges.

Referring to FIG. 8A, at block 802, one or more input tokens arereceived. In some embodiments, each input token of the set of one ormore input tokens includes (i.e., represents) one or more characters orone or more words (e.g., an individual character, a character sequence,a fragment of a word, a word, a fragment of a phrase, an entire phrase,a fragment of a sentence, an entire sentence, and the like), one or morephonemes (e.g., for speech recognition), or one or more spatialcoordinates (e.g., for handwriting recognition). In some embodiments,the set of one or more input tokens are obtained (e.g., parsed) from auser text input received at the electronic device from the one or moreinput devices. For example, the user text input may be received as agesture input (e.g., handwritten input), device keyboard input, speechinput (e.g., using a dictation service), peripheral device input, or acombination or sub-combination thereof. In some embodiments, the one ormore input tokens are displayed (e.g., by a display generation componentof the electronic device) in a text input field of a user interface. Forexample, the one or more input tokens may be displayed as a user isinputting text via a user interface for a messaging application, anotebook application, a search bar, or the like.

In response to receiving the one or more input tokens at block 802, atblock 804, a first set of one or more output tokens is predicted using afirst output token probability distribution drawn from a first overalltoken probability distribution of a first language model. As discussedabove, in some embodiments, the first output token probabilitydistribution (e.g., Y′) represents a probability distribution over anunderlying token vocabulary of the first language model at a specificpoint in time (e.g., specifically given the one or more input tokensreceived at block 802 (e.g., W)), while the first overall probabilitydistribution (e.g., D′) represents a probability distribution over allsuch probability distributions (e.g., over time). Accordingly, in theseembodiments, the first output token probability distribution is said tobe drawn from the first overall probability distribution.

In some embodiments, the first set of one or more output tokensrepresent a predicted correction to at least one of the one or moreinput tokens. For example, as illustrated in FIG. 7A, predicted token704A (“chickens”) is predicted as a corrected version of the final tokenof text input 702A (“Vegan chickn”). In some embodiments, the first setof one or more output tokens represent predicted next tokens followingthe one or more input tokens. For example, as illustrated in FIG. 7C,predicted token 704C (“lunch”) is predicted as the next token to followtext input 702C (“Vegan chick'n for”).

In some embodiments, in addition to predicting the first set of one ormore output tokens, a second set of one or more output tokens may bepredicted based on the first output token probability distribution(e.g., Y′), where a probability that the first set of one or more tokensis the next text input is greater than a probability that the second setof one or more tokens is the next text input. For example, based on theone or more input tokens “Vegan chickn” (e.g., as illustrated in FIG.7A), the first output token probability distribution (e.g., Y′) mayindicate a probability that the token “chicken” is the most likely nexttext input and a probability that the token “chickenpox” is the secondmost likely text input (e.g., as potential corrections to the inputtoken “chickn”). As the first output token probability distribution(e.g., Y′) is a probability distribution over the underlying tokenvocabulary of the first language model, in some embodiments, evenfurther predictions of output tokens can be made (e.g., a third-mostlikely prediction, a fourth-most likely prediction, and so forth).

At block 806, a predicted user action to be performed on the first setof one or more output tokens is generated. For example, as describedabove, the first language model may include a joint prediction model(such as an encoder-decoder model) that uses the first overall tokendistribution (e.g., D′) to make a text prediction (e.g., as describedabove), and an action classifier to predict the user's response to thetext prediction. In some embodiments, the predicted user action is apredicted acceptance of the first set of one or more output tokens. Forexample, with reference to FIG. 7A, the predicted user action may be aprediction that the user will accept predicted token 704A (“chickens”)as a correction to the final word of text input 702A (“Vegan chickn”).In some embodiments, the predicted user action is a predicted rejectionof the first set of one or more output tokens. For example, withreference to FIG. 7A, the predicted user action may be a prediction thatthe user will not accept predicted token 704A (“chickens”), forinstance, if the probability that predicted token 704A (“chickens”) isthe next text input fails to exceed a confidence threshold, or if theprobability that predicted token 704B (“chickenpox”) is the next textinput falls within a narrow margin of the probability of predicted token704A (“chickens”).

At block 808, an output including the first set of one or more outputtokens is provided. In some embodiments, the output may includeadditional sets of predicted output tokens, such as the second set ofoutput tokens discussed above. For example, as illustrated in FIG. 7C,an output indicating predicted token 704C (“lunch”), predicted token704D (“dinner”), and predicted token 704E (“the”) is provided, wherepredicted token 704C (“lunch”) is the first (e.g., most likely) set ofoutput tokens, predicted token 704D (“dinner”) is the second (e.g., nextmost likely) set of output tokens, and so forth (e.g., predicted token704E (“the”) is the third most likely prediction).

In some embodiments where the first set of one or more output tokensrepresent a set of predicted next tokens following the one or more inputtokens, providing the output includes providing a first affordanceindicating the first set of one or more output tokens which, ifselected, inserts the first set of one or more output tokens after theone or more input tokens in the text input field. For example, asillustrated in FIG. 7C, predicted token 704C (“lunch”) is indicated by aselectable affordance situated above a keyboard (e.g., as part of apredictive typing interface), such that selecting the affordance wouldinsert the word “lunch” after text input 702C (“Vegan chick'n for”). Insome embodiments, the first affordance may be selected with an explicituser input, such as a tap or click.

In some embodiments where the first set of one or more output tokensrepresent a correction to at least one token of the one or more inputtokens, providing the output includes providing a second affordanceindicating the first set of one or more output tokens which, ifselected, replaces the at least one input token with the first set ofone or more output tokens. For example, as illustrated in FIG. 7A,predicted token 704A (“chickens”) is indicated by a selectableaffordance situated above a keyboard (e.g., as part of a predictivetyping interface), and the text in the input field is highlighted toindicate that the input token “chickn” will be replaced if theaffordance is selected. As another example, the first set of one or moreoutput tokens may be indicated by an affordance situated in the textinput field, such as a pop-up correction situated adjacent to the inputtoken that would be replaced if the affordance is selected. In someembodiments, the second affordance may be selected with an explicit userinput, such as a tap or click.

In some embodiments where the first set of one or more output tokensrepresent a correction to at least one input token of the one or moreinput tokens, providing the output includes automatically replacing theat least one input token with the first set of one or more outputtokens. For example, if the first set of one or more output tokens areindicated by a pop-up affordance situated adjacent to an input token,and the user continues to input text without dismissing the pop-upaffordance, the input token may be automatically replaced (e.g., withoutrequiring an explicit user input selecting the pop-up affordance). Asanother example, an input token may be automatically replaced with thefirst set of one or more output tokens without first indicating thefirst set of one or more output tokens with an affordance, for instance,if the probability that the first set of one or more output tokens isthe next text input is very high (e.g., if the probability exceeds acertain confidence threshold).

Referring to FIG. 8B, at block 810, a first user action responding tothe first set of one or more output tokens is detected. In someembodiments, the first user action is a user input accepting the firstset of one or more output tokens. For example, referring to FIG. 7A, auser input selecting the affordance for predicted token 704A(“chickens”) is a first user action accepting the first set of one ormore output tokens (e.g., the best prediction). In some embodiments, thefirst user action is a user input not accepting the first set of one ormore output tokens. For example, referring to FIG. 7A, a user inputselecting the affordance for predicted token 704B (“chickenpox”) (e.g.,the second set of output tokens) is a first user action that does notaccept predicted token 704A (e.g., the first set of output tokens). Asanother example, if, instead of providing predicted token 704A(“chicken”) as an affordance, the portion of text input 702A “chickn”had been automatically replaced with predicted token 704A, a user inputmanually deleting the word “chicken” and replacing it with text input702B, “chick'n,” is a first user action that does not accept predictedtoken 704A. As another example, referring to FIG. 7D, the user inputmanually entering text input 702D, “kebabs,” after text input 702B(“Vegan chick'n for”) is a first user action that does not acceptpredicted token 704C (“lunch”) (e.g., the first set of output tokens).

In some embodiments, in response to detecting the first user actionresponding to the first set of one or more output tokens, adetermination is made whether the first user action matches thepredicted user action. A mismatch between the detected user action andthe predicted user action indicates that the first language model hasincorrectly anticipated user behavior. For example, if the actionclassifier of the first language model predicted that the user wouldaccept the first set of one or more output tokens as a prediction, butthe user rejects the first set of one or more output tokens (e.g., bymanually entering a different token or selecting a differentprediction), the mismatch may indicate that the first output tokenprobability distribution Y′ is too heavily skewed in favor of the firstset of one or more output tokens, or that the action classifier isoverly-confident in user acceptance of predictions. Accordingly, in someembodiments, this determination can be used to trigger an update to thefirst language model based on the unexpected user feedback, forinstance, as described below with respect to blocks 812-822.

In accordance with a determination, at block 812, that the first (e.g.,detected) user action does not match the predicted user action, at block814, a modified output token probability distribution is generated basedon the first (e.g., detected) user action. In some embodimentsimplementing a GAN (e.g., as described with respect to FIG. 6 ), themodified output token probability is an adjusted version of output tokenprobability distribution

(W).

In some embodiments, generating the modified output probabilitydistribution includes, at block 816, determining, based on the firstuser action, a set of one or more user-validated tokens. For example,referring to FIG. 7B, the token “chick'n” parsed from text input 702B isdetermined to be a user-validated token, as the user has explicitlyentered the token to correct text input 702A (“Vegan chickn”). Asanother example, referring to FIG. 7D, the token “kebabs” parsed fromtext input 702D is determined to be a user-validated token, as the userhas explicitly entered the token following text input 702C (“Veganchick'n for”).

In these embodiments, at block 818, a second output token probabilitydistribution is generated based on the set of one or more user-validatedtokens determined at block 816. For example, the second output tokenprobability distribution may be a sparse output token probabilitydistribution Z over an underlying token vocabulary, where the sparseoutput token probability distribution Z indicates that theuser-validated token is the anticipated next user input given the one ormore input tokens (e.g., given input context vector W).

In these embodiments, at block 820, an output token probabilitydistribution drawn from the first overall token probability distributionis modified based on the second output token probability distribution,generating the modified output token probability distribution used toupdate the first overall token probability distribution. For example, asdiscussed above, in embodiments implementing a GAN (e.g., as describedwith respect to FIG. 6 ), a generator component of the GAN can beinitialized with the first language model/first overall tokenprobability distribution. Accordingly, for an initial iteration of theupdate, the output token probability being modified is the first outputtoken probability distribution Y′ drawn from the first overall tokenprobability distribution D′ given the input context vector W (e.g., forthe initial iteration,

(W)=Y′). Accordingly, for the first iteration

(W)=Y′, the modified output token probability distribution Y″ iscalculated as:

$Y^{''} = {\left( (W) \right) = {\left( Y^{\prime} \right) = \frac{Y^{\prime} + {\lambda \cdot Z}}{{Y^{\prime} + {\lambda \cdot Z}}}}}$

where λ>0 is an optional weight that controls the importance of the userfeedback (e.g., the first user action and the user-validated token)relative to the prediction (e.g., the predicted user action and thefirst set of one or more tokens). The normalization ensures that thesecond output token probability distribution Y″ is a proper probabilitydistribution over the underlying token vocabulary of the first languagemodel. For subsequent iterations, the output token probabilitydistribution

(W) is drawn from the current (e.g., updated) iteration of the overalltoken probability distribution, and is modified according to the sameformula to generate the modified output token probability distribution.

At block 822, the first overall token probability distribution isupdated to converge to a second overall token probability distributionbased on the modified output token probability distribution. In someembodiments, the second overall token probability distribution is drawnfrom a second language model. For example, as described with respect toFIG. 6 , the second language model may be a target language modelincluding the second (e.g., target) overall token probabilitydistribution D. In some embodiments, the second language model has beentrained on a set of general training data, such as a very large corpusof text samples used to train language models in accordance with generalusage (e.g., rather than for a specific user). In some embodiments, thesecond language model is trained on a set of user data including datagenerated by a user of the device and data associated with the user ofthe electronic device. For example, the set of user data may includedata generated by the user (e.g., text input by the user) and/or dataassociated with the user (e.g., text the user has received, read, orotherwise interacted with). Accordingly, as the first overall tokenprobability distribution is updated to converge to the second overalltoken probability distribution, the updated first overall tokenprobability distribution reflects the user-specific training of thesecond overall token probability distribution.

In some embodiments implementing a GAN (e.g., as described with respectto FIG. 6 ), updating the first overall token probability distributionto converge to a second overall token probability distribution includestraining a discriminator to determine a probability that a given outputtoken probability is drawn from the second overall token probabilitydistribution. In these embodiments, the discriminator is used todetermine a probability that the modified output token probabilitydistribution is drawn from the second overall token probabilitydistribution. For example, may be trained to output

(Y)=1 given an output token probability distribution Y known to be drawnfrom the second (e.g., target) overall token probability distribution D,and trained to output

(Y′)=0 given output token probability distribution Y′ known not to bedrawn from D. A trained discriminator output of 0.5 thus indicates anequal probability that the observed output token probabilitydistribution is equally likely to have been drawn from drawn from D asit is to have been drawn from a different overall token probabilitydistribution.

In these embodiments, updating the first overall token probabilitydistribution to converge to a second overall token probabilitydistribution may include determining whether the probability that themodified output token probability distribution is drawn from the secondoverall token probability distribution satisfies convergence criteria.For example, the convergence criteria may require that the probabilitythat the modified output token probability distribution is drawn fromthe second overall token probability distribution falls within a narrowrange of 0.5. In accordance with a determination that the probabilitysatisfies the convergence criteria, a determination is made that thefirst overall token probability distribution has converged to the secondoverall token probability distribution.

Accordingly, in these embodiments, the generator iteratively updates thefirst overall token probability distribution of the first language modeluntil the modified output token probability distribution drawn from thecurrent (e.g., updated) iteration of the first overall token probabilitydistribution produces

(Y″) within a narrow range of 0.5 (e.g., an equal probability thatmodified output token probability distribution was drawn from D vs.not). The first overall token probability distribution can beiteratively updated to converge to the second overall token probabilitydistribution D by solving:

min max ( , ) = 𝔼 Y ∼ D ⁢ { log [ ( Y ) ] } + 𝔼 [ ( W ) ] ∼ D ″ ⁢ { log [1 - ( [ ( W ) ] ) ] }

where

(

,

) denotes an overall cost function of a minimax two-player game andwhere W represents an input of previous tokens (i.e., an input context).The output token probability distribution

(W) is modified (e.g., adjusted) according to the adjustment formulaabove, such that the solution is generated based on the modified outputtoken probability

[

(W)]. Accordingly, maximizing

over

while minimizing

over

ensures that, after enough iterations, the generator will have updatedthe first language model to the point where the modified output tokenprobability distribution Y″=

[

(W)], drawn from the current (e.g., updated) iteration of the firstoverall token probability distribution, is probabilistically similar toan output token probability distribution Y known to be drawn from thesecond (e.g., target) overall probability distribution D (e.g.,

(Y″) falls within a narrow margin of 0.5). At that point, the current(e.g., updated) iteration of the first overall token probabilitydistribution is considered to have converged with the second overalltoken probability distribution D. As

min max ( , )

is solved using the output probability distributions modified based onthe first user action (e.g.,

[

(W)]), the first overall token probability distribution is updated in away that reflects the first user action (e.g., the user feedback).

In some embodiments, in addition to updating the first overall tokenprobability distribution to converge to a second overall tokenprobability distribution, in accordance with a determination at block812 that the first user action does not match the predicted user action,a parameter of an action prediction module of the first language model.As discussed above, in some embodiments, the first language modelincludes a joint prediction model that uses the first overall tokendistribution to make text predictions, and an action classifier topredict the user's response to the text prediction. Accordingly, inaddition to updating the first overall token distribution (e.g., asdescribed above with respect to blocks 814-822), in some embodiments, aparameter of the action classifier is also updated based on the firstuser action (e.g., the unexpected detected user action).

The foregoing description, for purpose of explanation, has beendescribed with reference to specific embodiments. However, theillustrative discussions above are not intended to be exhaustive or tolimit the invention to the precise forms disclosed. Many modificationsand variations are possible in view of the above teachings. Theembodiments were chosen and described in order to best explain theprinciples of the techniques and their practical applications. Othersskilled in the art are thereby enabled to best utilize the techniquesand various embodiments with various modifications as are suited to theparticular use contemplated.

Although the disclosure and examples have been fully described withreference to the accompanying drawings, it is to be noted that variouschanges and modifications will become apparent to those skilled in theart. Such changes and modifications are to be understood as beingincluded within the scope of the disclosure and examples as defined bythe claims.

As described above, one aspect of the present technology is thegathering and use of data available from various sources to improve theupdate of a language model used for text prediction. The presentdisclosure contemplates that in some instances, this gathered data mayinclude personal information data that uniquely identifies or can beused to contact or locate a specific person. Such personal informationdata can include demographic data, location-based data, telephonenumbers, email addresses, social network IDs, home addresses, data orrecords relating to a user's health or level of fitness (e.g., vitalsigns measurements, medication information, exercise information), dateof birth, or any other identifying or personal information.

The present disclosure recognizes that the use of such personalinformation data, in the present technology, can be used to the benefitof users. For example, the personal information data can be used toupdate the language model used for text prediction. Accordingly, use ofsuch personal information data enables users to have text predictionthat reflects their specific preferences and tendencies. Further, otheruses for personal information data that benefit the user are alsocontemplated by the present disclosure. For instance, health and fitnessdata may be used to provide insights into a user's general wellness, ormay be used as positive feedback to individuals using technology topursue wellness goals.

The present disclosure contemplates that the entities responsible forthe collection, analysis, disclosure, transfer, storage, or other use ofsuch personal information data will comply with well-established privacypolicies and/or privacy practices. In particular, such entities shouldimplement and consistently use privacy policies and practices that aregenerally recognized as meeting or exceeding industry or governmentalrequirements for maintaining personal information data private andsecure. Such policies should be easily accessible by users, and shouldbe updated as the collection and/or use of data changes. Personalinformation from users should be collected for legitimate and reasonableuses of the entity and not shared or sold outside of those legitimateuses. Further, such collection/sharing should occur after receiving theinformed consent of the users. Additionally, such entities shouldconsider taking any needed steps for safeguarding and securing access tosuch personal information data and ensuring that others with access tothe personal information data adhere to their privacy policies andprocedures. Further, such entities can subject themselves to evaluationby third parties to certify their adherence to widely accepted privacypolicies and practices. In addition, policies and practices should beadapted for the particular types of personal information data beingcollected and/or accessed and adapted to applicable laws and standards,including jurisdiction-specific considerations. For instance, in the US,collection of or access to certain health data may be governed byfederal and/or state laws, such as the Health Insurance Portability andAccountability Act (HIPAA); whereas health data in other countries maybe subject to other regulations and policies and should be handledaccordingly. Hence different privacy practices should be maintained fordifferent personal data types in each country.

Despite the foregoing, the present disclosure also contemplatesembodiments in which users selectively block the use of, or access to,personal information data. That is, the present disclosure contemplatesthat hardware and/or software elements can be provided to prevent orblock access to such personal information data. For example, in the caseof updating a language model used for text prediction, the presenttechnology can be configured to allow users to select to “opt in” or“opt out” of participation in the collection of personal informationdata during registration for services or anytime thereafter. In anotherexample, users can select not to provide mood-associated data forupdating language models. In yet another example, users can select tolimit the length of time mood-associated data is maintained or entirelyprohibit the development of a baseline mood profile. In addition toproviding “opt in” and “opt out” options, the present disclosurecontemplates providing notifications relating to the access or use ofpersonal information. For instance, a user may be notified upondownloading an app that their personal information data will be accessedand then reminded again just before personal information data isaccessed by the app.

Moreover, it is the intent of the present disclosure that personalinformation data should be managed and handled in a way to minimizerisks of unintentional or unauthorized access or use. Risk can beminimized by limiting the collection of data and deleting data once itis no longer needed. In addition, and when applicable, including incertain health related applications, data de-identification can be usedto protect a user's privacy. De-identification may be facilitated, whenappropriate, by removing specific identifiers (e.g., date of birth,etc.), controlling the amount or specificity of data stored (e.g.,collecting location data a city level rather than at an address level),controlling how data is stored (e.g., aggregating data across users),and/or other methods.

Therefore, although the present disclosure broadly covers use ofpersonal information data to implement one or more various disclosedembodiments, the present disclosure also contemplates that the variousembodiments can also be implemented without the need for accessing suchpersonal information data. That is, the various embodiments of thepresent technology are not rendered inoperable due to the lack of all ora portion of such personal information data. For example, a languagemodel can be updated by inferring preferences based on non-personalinformation data or a bare minimum amount of personal information, suchas the content being requested by the device associated with a user,other non-personal information available to the language model, orpublicly available information.

What is claimed is:
 1. An electronic device, comprising: one or moreprocessors; a memory; and one or more programs, wherein the one or moreprograms are stored in the memory and configured to be executed by theone or more processors, the one or more programs including instructionsfor: receiving one or more input tokens; in response to receiving theone or more input tokens, predicting, using a first output tokenprobability distribution drawn from a first overall token probabilitydistribution of a first language model, a first set of one or moreoutput tokens; generating a predicted user action to be performed on thefirst set of one or more output tokens; providing an output includingthe first set of one or more output tokens; detecting a first useraction responding to the first set of one or more output tokens; and inaccordance with a determination that the first user action does notmatch the predicted user action: generating a modified output tokenprobability distribution based on the first user action; and based onthe modified output token probability distribution, updating the firstoverall token probability distribution to converge to a second overalltoken probability distribution.
 2. The electronic device of claim 1, theone or more programs further including instructions for: predicting asecond set of one or more output tokens based on the first output tokenprobability distribution, wherein a first probability of the first setof one or more output tokens is greater than a second probability of thesecond set of one or more output tokens.
 3. The electronic device ofclaim 1, wherein the one or more input tokens are displayed in atext-input field of a user interface.
 4. The electronic device of claim3, wherein: the first set of one or more output tokens represent a setof predicted next tokens following the one or more input tokens; andproviding the output including the first set of one or more outputtokens includes providing a first affordance indicating the first set ofone or more output tokens, which, if selected, inserts the first set ofone or more output tokens after the one or more input tokens in thetext-input field.
 5. The electronic device of claim 3, wherein the firstset of one or more output tokens represent a correction to at least oneinput token of the one or more input tokens.
 6. The electronic device ofclaim 5, wherein: providing the output including the first set of one ormore output tokens includes providing a second affordance indicating thefirst set of one or more output tokens, which, if selected, replaces theat least one input token with the first set of one or more outputtokens.
 7. The electronic device of claim 5, wherein: providing theoutput including the first set of one or more output tokens includesautomatically replacing the at least one input token with the first setof one or more output tokens.
 8. The electronic device of claim 1,wherein the predicted user action is a predicted acceptance of the firstset of one or more output tokens.
 9. The electronic device of claim 1,wherein the predicted user action is a predicted rejection of the firstset of one or more output tokens.
 10. The electronic device of claim 1,wherein the first user action is a user input accepting the first set ofone or more output tokens.
 11. The electronic device of claim 1, whereinthe first user action is a user input not accepting the first set of oneor more output tokens.
 12. The electronic device of claim 1, whereingenerating the modified output token probability distribution based onthe first user action includes: determining, based on the first useraction, a set of one or more user-validated tokens; generating, based onthe set of one or more user-validated tokens, a second output tokenprobability distribution; and modifying an output token probabilitydistribution drawn from the first overall token probability distributionbased on the second output token probability distribution to generatethe modified output token probability distribution.
 13. The electronicdevice of claim 1, the one or more programs further includinginstructions for: in accordance with a determination that the first useraction does not match the first user action, modifying a parameter of anaction-prediction module of the first language model.
 14. The electronicdevice of claim 1, wherein the second overall token probabilitydistribution is drawn from a second language model.
 15. The electronicdevice of claim 14, wherein the second language model is trained on aset of user data including data generated by a user of the device anddata associated with the user of the electronic device.
 16. Theelectronic device of claim 1, wherein updating the first overall tokenprobability distribution to converge to the second overall tokenprobability distribution includes: training a discriminator to determinea probability that a given output token probability is drawn from thesecond overall token probability distribution; and determining, usingthe discriminator, a probability that the modified output tokenprobability distribution is drawn from the second overall tokenprobability distribution.
 17. The electronic device of claim 16, whereinupdating the first overall token probability distribution to converge tothe second overall token probability distribution includes: determiningwhether the probability that the modified output token probabilitydistribution is drawn from the second overall token probabilitydistribution satisfies convergence criteria; and in accordance with adetermination that the probability that the modified output tokenprobability distribution is drawn from the second overall tokenprobability distribution satisfies convergence criteria, determiningthat the first overall token probability distribution has converged tothe second overall token probability distribution.
 18. A non-transitorycomputer-readable storage medium storing one or more programs configuredto be executed by one or more processors of an electronic device, theone or more programs including instructions for: receiving one or moreinput tokens; in response to receiving the one or more input tokens,predicting, using a first output token probability distribution drawnfrom a first overall token probability distribution of a first languagemodel, a first set of one or more output tokens; generating a predicteduser action to be performed on the first set of one or more outputtokens; providing an output including the first set of one or moreoutput tokens; detecting a first user action responding to the first setof one or more output tokens; and in accordance with a determinationthat the first user action does not match the predicted user action:generating a modified output token probability distribution based on thefirst user action; and based on the modified output token probabilitydistribution, updating the first overall token probability distributionto converge to a second overall token probability distribution.
 19. Amethod, comprising: at an electronic device with one or more processorsand memory: receiving one or more input tokens; in response to receivingthe one or more input tokens, predicting, using a first output tokenprobability distribution drawn from a first overall token probabilitydistribution of a first language model, a first set of one or moreoutput tokens; generating a predicted user action to be performed on thefirst set of one or more output tokens; providing an output includingthe first set of one or more output tokens; detecting a first useraction responding to the first set of one or more output tokens; and inaccordance with a determination that the first user action does notmatch the predicted user action: generating a modified output tokenprobability distribution based on the first user action; and based onthe modified output token probability distribution, updating the firstoverall token probability distribution to converge to a second overalltoken probability distribution.